Systems and methods for customizing monitoring programs involving remote devices

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizing monitoring programs involving remote devices. In some implementations, at least one of subject attribute data describing characteristics of subjects or subject outcome data including results from monitoring programs that involved the subjects are extracted from a subject database. The subjects are grouped into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects. A profile is constructed for a group which includes defining inclusion criteria for the group based on aggregate data for the subjects in the group. Data sets are used in the subject database to characterize, for each profile, differing effects of elements of monitoring programs on program compliance outcomes for subjects that satisfy the criteria of the profile. The characterization data is used to create or adjust a monitoring program that involves communicating with a selected set of remote devices over the communication network.

FIELD OF INVENTION

This application generally relates to the evaluation, selection, and monitoring of groups to be monitored in monitoring programs.

BACKGROUND

Many different types of monitoring and analysis involve sampling subsets of the total population of devices or other subjects to be monitored. This can provide high efficiencies and allow insight to the larger group from observation of a much smaller group. However, in some cases, the group selected to be monitored may not accurately reflect the composition of the population from which it is sampled. A mismatch between the characteristics or composition of a monitored group and a reference group can reduce the usefulness of the observations. In some cases, a monitored group with an improper composition can result in significant waste and inefficiency, as resources are expended in monitoring that is ineffective or fails to achieve the monitoring objectives.

SUMMARY

In some implementations, a computer system provides a platform for administering monitoring programs that involve customized data collection from groups of remote devices. The system enables different parties, e.g., organizations, administrators, third-parties, etc., to create or register monitoring programs to be distributed and managed by the system. For example, a server system can provide a multi-tenant system that provides functionality to create monitoring programs each tenant organization, select groups of devices and users to participate in the monitoring programs, and distribute the monitoring programs (e.g., including items such as software modules, configuration data, data collection instructions, interactive content, etc. so that groups of remote devices carry out the monitoring). The system can thus administer many different monitoring programs on behalf of different tenant organizations, with the system including servers that collect and process data from the remote devices as the monitoring programs are carried out over time. The system can also monitor the effectiveness of the various monitoring programs, and can recommend adaptations to the monitoring programs and the monitoring groups if the system determines that compliance with program requirements is low or is predicted to be low. Many of the monitoring programs involve consistent, repeated interactions and data collection actions over a period of time (e.g., weeks, months, or even years).

As discussed below, the present system provides management functions for that are superior to typical application stores and other systems that provide interactions over the Internet. A typical application store providing software for mobile phones, for example, provides as many software applications as users desire, but does not track subsequent use of the applications and whether the usage and data provided meets standards and criteria for the applications, much less take actions to improve the usage rates and data quality. Nor is a typical application store configured to track and achieve different application-specific goals, such as targeted levels of diversity among devices and users using different applications, or to adjust applications to avoid or compensate for biases or flaws that disproportionately affect different devices or users. By contrast, the present system does track the extent that users and devices in the monitoring group for a monitoring program comply with the requirements of the monitoring program over time. Each monitoring program can have a defined group of devices and users involved in monitoring programs are often committed to ongoing participation in the monitoring program (e.g., subscribed, enrolled, or otherwise registered). While a monitoring group can change over time, the system can evaluate each monitoring group continually to ensure that the group's current composition and projected future composition (as well as data quality, compliance with requirements, etc.) all meet the overall goals for the monitoring program.

The participation in a monitoring program typically requires a set of actions, both for data collection and separate from data collection, that need to be performed to comply with the requirements of the monitoring program. The success of the monitoring program as a whole can depend on having a monitoring group with at least a minimum level of diversity (e.g., variation or variety in different attributes or categories), where devices and/or users for each of different profiles or contexts each comply with the required actions over time. The system can determine whether the usage and data collected meets the particular standards and criteria for each monitoring program, as well as take actions to improve the usage rates and data quality for the monitoring group. The system stores data indicating the goals and requirements for a monitoring program as a whole, such as a need to achieve a targeted level of diversity among devices and users. The system can detect elements of monitoring programs that disproportionately affect different devices or users, and can identify changes to adjust the monitoring programs to avoid or compensate for those biases. The system can also select a monitoring group, or determine changes to a monitoring group, to achieve compliance with the monitoring program requirements by at least the needed minimum of participants from each of the different groups or categories.

These and other functions make the present system more effective by raising the percentage of participating devices and users that are retained in and comply with requirements of a monitoring program. This also improves the efficiency in the allocation of computational resources, because the rate of successful completion of monitoring programs increases significantly, limiting the additional time, processing, network bandwidth, and other resources that would be needed to re-start a failed program or extend a monitoring program that has not acquired sufficient data. The system can define target characteristics for the composition of a monitoring group, including the level of diversity or distribution among different groups or backgrounds. The system can repeatedly verify, for each monitoring program, whether successful monitoring is occurring for sufficient numbers of devices or users for the respective groups of backgrounds, and use various actions (e.g., monitoring program changes, monitoring group changes, changed communication, etc.) as feedback to bring the level of diversity. This allows the system to manage each monitoring program at the aggregate level, to achieve desired characteristics (e.g., diversity or distribution of characteristics) in the monitoring group as a whole and consequently for the aggregate data set that will result from the monitoring program. Beyond monitoring the compliance of individual members of a monitoring group, the system detects changes or risks to completion of the objective of the monitoring program (including potential lack of diversity or representation among some subjects to be monitored) recommends and carries out actions to proactively avoid those risks.

The system provides interfaces that provide the functionality to design and update monitoring programs that involve collection of sensor data, user input, and other interactions using remote devices. The various monitoring programs that the system administers can each have their own respective characteristics and requirements. For example, different monitoring programs may have different objectives or purposes, and so may very different parameters, e.g., duration of the program (e.g., 1 month, three months, etc.), frequency of data collection (e.g., hourly, daily, weekly, etc.), latency for sending results, types of data to be collected, types of sensors to be used, types of user interactions or user inputs needed during monitoring, and so on. The system can concurrently manage the data collection and data processing for different monitoring programs, generating separate data sets for each monitoring program and performing individual communications with each remote device for the specific monitoring program(s) that the remote device is involved in.

One of the important aspects of a monitoring program is the set of devices and users that participate in the monitoring program, referred to generally as the “monitoring group” for a monitoring program. In many situations, the monitoring group for a monitoring program must have certain size and composition characteristics in order for the monitoring to be effective. If the monitoring group is too small or has a composition that is too homogenous, the monitoring results may be inaccurate or incomplete. Even worse, in many cases, if the composition of the monitoring group deviates significantly from required characteristics, the results of the monitoring program may be unusable, meaning that the computing resources of client devices and servers (e.g., processing capability, storage capacity, power, network bandwidth, etc.) expended in in monitoring over weeks, months, or years has been wasted. Systems and users that rely on effective monitoring may expect that monitoring is being performed appropriately (perhaps due to a large number of monitored devices and users), only to find later that monitoring was ineffective due to a skewed composition of the monitoring group, which may have failed to detect and quantify many contexts, situations, and events that should have been monitored.

The present system manages monitoring programs in a way that improves on previous systems with the ability to evaluate the composition of monitoring groups and actually improve the composition of and compliance of monitoring groups to meet the needs of their associated monitoring programs. The system does this in several ways, such as by guiding the initial selection of subjects to include in the monitoring group, adaptively adding and adjusting membership in the monitoring group, adjusting the characteristics of a monitoring program to avoid or remove bias against compliance by certain groups, and predicting the resulting compliance, retention, and data quality that will be achieved by the end of a monitoring program (e.g., at a scheduled end date or at a certain amount of time in the future. It is not sufficient to simply enroll a group that has the proper size and composition (including diversity), the needed number of participants and diversity of participants need to comply with monitoring program requirements, provide sufficiently complete and accurate data, and be retained until the end of the monitoring program. To assist with this, the system can predict, for individuals or groups, the level or extent of compliance with requirements, data quality for collected data, retention to the end of a predetermined time period, and other properties, and use those predictions for updating user interfaces as well as to adjust monitoring programs and monitoring groups that the system manages.

Each monitoring program may have a set of selection criteria that are used to determine the eligibility of devices and/or users to participate in a monitoring program. The selection criteria can include inclusion criteria that specify characteristics that are needed in order to participate, as well as exclusion criteria that specify characteristics that disqualify a user or device from participating. The selection criteria can be applied by the system to identify candidates or confirm that participants should be added to the monitoring group for a monitoring program (e.g., enrolled in the monitoring program and remote monitoring initiated). However, even if the devices and users selected for a monitoring group each individually meet the selection criteria, that does not ensure that the monitoring group as a whole has the characteristics needed for the monitoring program to be successful (e.g., to provide a predetermined minimum level of statistical validity or confidence). Accordingly, the systems and techniques describes herein allow more sophisticated evaluation and adaptation of monitoring programs (e.g., required actions, data collection, monitoring groups, etc.) so that the system guides the monitoring program to achieve not only data collection by a minimum number of participants, but also to have valid data collection over the course of the monitoring program from at least the minimum numbers for participants corresponding to each of various different profiles or backgrounds.

The system is configured to distribute software for monitoring programs to devices that are used to perform the monitoring. The monitoring program may specify parameters for monitoring, such as times when data is to be obtained from, requested from, or sent to the devices. The monitoring program may also indicate the type of data or the specific data that is to be obtained from, requested from, or sent to the devices. The data obtained or requested from the devices may include sensor data collected using sensors of the devices or from other devices (e.g., connected through wired or wireless links). The data that the system sends to the remote devices may include instructions to collect sensor data, or updates to the monitoring program or a portion of the monitoring program on the devices. In updating software used to implement a monitoring program on the remote devices, the configuration of the remote devices can be adjusted, for example, to change what data is collected, change how the data is collected, change communication network settings, etc.

In some implementations, the computer system adjusts individual monitoring programs and distributes changes to devices for each program's monitoring group. Even within the monitoring group of a single monitoring program, the system may select different settings, interactions, or content to provide to different devices. In some cases, the interactions vary depending which of various profiles the devices correspond to. The computer system may assign each of the device to at least one profile based on attributes of the devices or their users. For example, a first profile may be used for devices that include a GPS unit and a heartrate monitor, and that have a user that lives in an urban environment and is between 25 and 30 years of age. If a first device meets the device requirements of the profile and has a user that meets the user requirements of the profile, the system may classify the first device as corresponding to the first profile and the system interacts with that device using the information in that profile.

In some implementations, the system generates the profiles based on previously observed outcomes for devices and users in other monitoring programs. For example, the system may generate profiles based on observed outcomes of currently ongoing and/or previously completed monitoring programs. The observed outcomes may include the compliance of the devices or their users with the requirements of the monitoring program, and the retention of the devices or their users in the monitoring program. As another example, the system may generate profiles based on attributes of devices and/or users in a set, such as a set of devices and/or users that have participated in one or more monitoring programs. The attributes may include, for example, sensors that the devices include, sensor devices that are compatible with the devices, models of the devices, operating systems of the devices, etc. The attributes may also include demographic or non-demographic information that describes the users. The users may include, for example, users that have previously participated in a monitoring program, that are currently participating in a monitoring program, have indicated that they want to participate in a monitoring program, or that are eligible for a monitoring program.

In some implementations, the system generates the profiles using a machine learning model or a group of machine learning models. As an example, the computer system may using a clustering machine learning model to cluster different devices or users based on observed outcomes. Similarly, as another example, the system may use a clustering model to cluster different groups of devices or users based on attributes of the devices or users. The model may use any or all attributes available to the model in performing the clustering. Alternatively, the model may use a subset of attributes corresponding to key attributes to perform the clustering. These key attributes may be determined using another machine learning model or a group of other machine learning models, using a static algorithm or group of static algorithms, or based on input from an administrator or researcher.

In some implementations, the system uses the profiles to create or adjust the monitoring group for a monitoring program. As an example, in response to determining that a monitoring group has less than a minimum threshold number of devices corresponding to a first profile, the system may add a device or user that meets the criteria for the first profile. The profiles can provide a way for the system to diversify a monitoring group or achieve a better distribution of monitored devices and users across different profiles or sets of characteristics. Adjusting the monitoring group may be based on the level of diversity in a monitoring group. The profiles, or a subset of the profiles, may correspond to different groups or categories that are assessed during diversity analysis. The system may determine that criteria specifying minimum or target levels of diversity for the monitoring program are not met when, for example, the monitoring group does not include any members from a certain group or profile, or if there is an insufficient amount (e.g., number, proportion, etc.) of members that correspond to the group or profile. For example, the system may determine that one or more profiles or categories of devices or users are not represented in or are underrepresented in a monitoring group, and, in response, the system can adjust the monitoring group to include additional device(s) that correspond to the one or more profiles or categories.

The system may analyze diversity at one or more points during the lifecycle of a monitoring program (e.g., defining the program, selecting the initial monitoring group, while data collection is ongoing, etc.). As an example, in a research study, if a device corresponding to a first group fails to provide the needed data, the system may determine whether the diversity targets for the research study are still met through data that is successfully collected from other devices. For example, the diversity criteria may specify that at least a minimum number or percentage of the participants should be from a first category, and the system can verify that the minimum is still satisfied by other devices and users that are providing data in compliance with the study's requirements. If the diversity criteria are not met, the system may generate a recommendation to correct or improve the diversity of the monitored group, or may automatically perform another action. For example, the system may automatically select an additional device of user corresponding to the first category to add to the monitoring group, and may transmit the software, configuration data, content, or other elements needed for the additional device to begin the needed monitoring.

In some implementations, the system is configured to perform a diversity assessment for a monitoring program and perform actions such as providing alerts, providing recommendations, and changing the monitoring program in response. In performing the diversity assessment, the computer system may identify the breakdown of a monitoring group among different categories or profiles, e.g., determining the numbers or proportions of each of different categories or profiles in the monitoring group. This information is referred to below as the “group composition” for a monitoring group, which can include data indicating which groups (e.g., categories, profiles, etc.) make up the monitoring group and in which amounts or proportions they occur (e.g., 10% in group 1, 30% in group 2, 0% in group 3, etc.). The groups or categories can be defined using many different attributes, including demographic and non-demographic characteristics. For example, different categories or profiles can be defined based on combinations of attribute values in different ranges, for example, for device types, device capabilities, user demographic characteristics (e.g., age, sex, race, occupation, educational level, etc.), geographic location, health status (e.g., diseases, physiological measurements, etc.).

After identifying the group composition for a monitoring group, the computer system may compare the group composition to a composition target to determine if the group composition is deviates from the composition target and to what extent. When a deviation of at least a predetermined amount is detected, the computer system may generate a warning for a researcher or administrator associated with the monitoring program, determine an action to correct the group composition, and/or generate a recommendation to perform the action or automatically perform the action. The system may proceed to generate a diversity report that it transmits to a client device over a communication network.

In some implementations, the diversity report may include the identified group composition (e.g., identifying groups represented, as well as warnings, recommended actions, and/or an indication of actions automatically performed by the system to better align to the diversity target. A researcher may use the information in the diversity report to make changes to the study, such as by changing the study parameters, adding or removing study criteria, inviting additional subjects to enroll in the study, enrolling additional participants Similarly, the system may make these changes automatically or in response to receiving a confirmation from the researcher. The changes made to the study may be targeted to or customized for to certain groups of a population, such as certain groups within a candidate pool, enrolled participants, and/or enrolled and active participants.

In one general aspect, a method includes: extracting, from a subject database, at least one of (i) subject attribute data describing characteristics of the subjects or (ii) subject outcome data including results from monitoring programs that involved the subjects; grouping the subjects, based on the extracted data from the database, into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects; constructing a profile for each of the groups, where constructing the profile for a group includes defining inclusion criteria for the group based on aggregate data for the subjects in the group; using data sets in the subject database to characterize, for each profile, differing effects of elements of monitoring programs on program compliance outcomes for subjects that satisfy the criteria of the profile; and using the characterization data to create or adjust a monitoring program involving communicating with a selected set of remote devices over a communication network.

Implementations include one or more of the following features. For example, in some implementations, grouping the subjects into the different groups according to the levels of similarity among the attributes of the subjects or the monitored outcomes for the subjects using at least one machine learning model includes: providing at least one of the attributes of the subjects or the monitored outcomes for the subjects as input to a machine learning model; and using the machine learning model to cluster the subjects based on at least one of the attributes of the subjects or the monitored outcomes for the subjects, where the resulting clusters corresponds to the different groups of subjects.

In some implementations, the machine learning model includes a clustering model.

In some implementations, the clustering model is a k-means clustering model.

In some implementations, the clustering model is a density clustering model, a connectivity clustering model, a centroid clustering model, distribution clustering model, a subspace clustering model, a group clustering model, a graph clustering model, signed-based clustering model, or a neural network model.

In some implementations, using the characterization data to create or adjust the monitoring program includes: identifying a set of elements of the monitoring program; identifying a subset of profiles corresponding to the set of remote devices; using the set of elements and the subset of profiles to determine one or more negative effects on program compliance outcomes of the monitoring program; and based on the one or more negative effects, determining an adjustment to at least one of the monitoring program or to the set of remote devices to counter the one or more negative effects.

In some implementations, determining the adjustment to at least one of the monitoring program or to the set of remote devices includes determining to adjust the monitoring program by adding a study element to the monitoring program to compensate for a predicted impact of at least one element in the set of elements.

In some implementations, determining the adjustment to at least one of the monitoring program or to the set of remote devices includes determining to adjust the monitoring program by removing or modifying a study element to the monitoring program to compensate for a predicted impact of at least one element in the set of elements.

In some implementations, determining the adjustment to at least one of the monitoring program or to the set of remote devices includes determining to (i) add one or more remote devices to the set of remote devices, or (ii) switching out one or more of the remote device in the set of remote devices for one or more other remote devices.

In some implementations, determining the adjustment to at least one of the monitoring program or to the set of remote devices includes, for each profile in the subset of profiles, generating a new version of the monitoring program that is applicable to corresponding subsets of devices in the set of remote devices.

In another general aspect, a method includes: accessing, by the one or more computers, subject data in a database for subjects that have been involved in one or more monitoring programs, the subject data includes attribute data indicating attributes of subjects and outcome data indicating monitored outcomes for the subjects monitored during one or more monitoring programs that involve collection of data for each subject over a communication network; determining, by the one or more computers, subsets of the subjects based on a degree of similarity in at least attributes of the subjects or monitored outcomes for the subjects; defining, by the one or more computers, a plurality of profiles based on the determined subsets, where each of the profiles represents a different class of subjects having attributes that satisfy criteria specified in the profile; generating, by the one or more computers, impact characterization data indicating different levels of impact that elements of monitoring programs have on outcomes for subjects that satisfy the criteria of different profiles, the impact characterization being generated based on analysis of the outcome data for different groups of subjects that correspond to different profiles; and using, by the one or more computers, the plurality of profiles and the generated impact characterization data to create a monitoring program or adjust a monitoring program involving communicating with a selected set of remote devices over the communication network.

In some implementations, the method includes adding a study element to the monitoring program based on the impact characterization data, where the study element is added to compensate for a predicted impact of at least one other element in the monitoring program.

In some implementations, the method includes: generating a new version of the monitoring program for a particular profile of the different profiles; and adding a study element to a monitoring program or modifying a study element in the monitoring program, where the study element is added or modified to compensate for a predicted impact of at least one other element in the monitoring program based on a portion of the impact characterization data associated with the particular profile of the different profiles.

In some implementations, customizing the monitoring program for each of the different profiles based on portions of the impact characterization data corresponding to each of the different profiles.

In some implementations, storing portions of the impact characterization data corresponding to the different profiles in each of the profiles.

In some implementations, generating the impact characterization data includes generating at least one of scores, likelihoods, and percentage rates, where the scores, likelihoods, and percentages correspond to determined impacts that the elements of monitoring programs have on outcomes for subjects that satisfy the criteria of different profiles.

In some implementations, generating the at least one of scores, likelihoods, and percentage rates includes: generating a set of scores for each of the profiles; generating a set of likelihoods for each of the profiles; or generating a set of percentage rates for each of the profiles.

In some implementations, determining subsets of the subjects includes using at least one machine learning model to determine the subsets of the subjects based on at least one of the attributes of the subjects or the monitored outcomes for the subjects.

In some implementations, using at least one machine learning model to determine the subsets of the subjects includes: providing at least one of the attributes of the subjects or the monitored outcomes for the subjects as input to a machine learning model of the at least one machine learning model; and using the at least one machine learning model to cluster the subjects based on at least one of the attributes of the subjects or the monitored outcomes for the subjects, where each of the resulting clusters corresponds to a subset of the subjects.

In one general aspect, a method includes determining, by the one or more computers, a target composition for a group of devices to be monitored in a monitoring program, where the monitoring program involves collecting data from geographically distributed devices over a network, where the target composition is determined based on the composition of a heterogeneous reference set of devices in a geographical area, the target composition providing a target level of diversity among the devices in the group for each of multiple attributes; determining, by the one or more computers, a group composition for a group of devices that includes at least one of candidate devices for the monitoring program or enrolled devices that are enrolled to be monitored in the monitoring program; determining, by the one or more computers, that the group composition differs from the target composition in a manner that the group composition does not achieve target level of diversity for at least one attribute of the multiple attributes; in response to determining that the group composition provides lower diversity than the target composition for the at least one attribute, selecting, by the one or more computers, one or more actions that are configured to adjust the composition of the group to achieve the target level of diversity for the at least one attribute; and providing, by the one or more computers, output indicating the selected one or more actions that are selected to adjust the composition of the group to achieve the target level of diversity in the group.

Implementations may include one or more of the following aspects. For example, in some implementations, the group of devices include computer network infrastructure devices including one or more of routers, firewalls, switches, servers, load-balancers, intrusion detection systems, and storage area networks.

In some implementations, the target composition provides a target level of diversity across each of geographical location, device load level, hardware configuration, and software configuration.

In some implementations, the method includes: carrying out the selected one or more actions to adjust the composition of the group toward the target composition; and monitoring the adjusted group by performing repeated communications with each of the devices in the adjusted group over a period of time to collected predetermined data from the devices in the adjusted group.

In some implementations, the one or more actions are selected based on output from a machine learning model that is configured to predict, where the machine learning model includes a trained neural network or a trained classifier.

In another general aspect, a method includes: determining, by the one or more computers, a target level of diversity for a monitoring program based on a composition of a reference group, where the monitoring program involves collecting data from a set of geographically distributed devices over a network; determining, by the one or more computers, a group level of diversity for a group based on the composition of the group, where the group includes at least one of candidates or participants for the monitoring program; determining, by the one or more computers, that the group level of diversity does not provide the target level of diversity; in response to determining that the group level of diversity does not provide the target level of diversity, selecting, by the one or more computers, one or more actions that are configured to adjust the composition of the group to achieve the target level of diversity in the group; and providing, by the one or more computers, output data that indicates the selected one or more actions to adjust the composition of the group toward the target level of diversity for the group.

Implementations may include one or more of the following aspects. For example, in some implementations, the one or more actions are selected based on output of a machine learning model.

In some implementations, the target level of diversity is based on a projected future composition of the reference group.

In some implementations, the reference group is a group in a particular geographic area.

In some implementations, the reference group is a group that has a particular health condition or a risk of developing the particular health condition.

In some implementations, the target level of diversity indicates levels of composition for one or more age, sex, race, or ethnicity.

In some implementations, the selected one or more actions includes providing support or interaction to a subset of the group, the support or interaction being customized for the needs of the subset.

In some implementations, the selected one or more actions includes and action to broaden inclusion criteria for the cohort to encompass more of individuals having background that improves the diversity of the group.

In some implementations, the selected one or more actions includes and action to remove or narrow exclusion criteria for the cohort.

In some implementations, the method includes identifying an element of the monitoring program that results in a bias in the group, where the selected one or more actions are configured to reduce or eliminate the bias.

In some implementations, the one or more actions are configured to adjust the composition of the group to achieve the target level of diversity in the group.

The described techniques can be used to realize various advantages. The system provides active evaluation of the progress of monitoring programs over time, ensuring that the data collection and compliance with program requirements are sufficient to meet the objectives of the monitoring program. This enables the system to verify that not only the nominal size and composition of the monitoring group is sufficient, but that the realized compliance, retention, and data collection quality will result in a successful monitoring program. For example, a monitoring program begins with 100 enrolled participants, 50 in group 1 and 50 in group 2, where the program needs a minimum of 40 active participants in each of the two groups at the end of the three-month monitoring period. The system stores data specifying these requirements and then on a regular basis, for example, daily, check whether the data collection and participant actions yield the characteristics that would meet the requirements for the study as a whole. Even if all 100 participants nominally remain in the study, the system may determine that in the first month 7 of the participants in group 1 provided incomplete or inaccurate data. Detecting this trend or pattern of decreasing compliance in group 1, the system can predict that this presents above a threshold likelihood that the minimums for group 1 will not be met at the end of the three-month program period. The system can take various actions to address the issue, including: alerting a researcher associated with the program, changing the interaction some or all members of group 1, adding or recommending to add additional members of group 1, changing the data collection actions for members of group 1 to increase compliance, changing elements of the program (e.g., substituting a requirement for an in-person visit with one for a phone call), and so on. These changes may ensure or, at least, improve the likelihood that the one or more set goals for the monitoring program are achieved.

For example, the system can manage monitoring programs by using subject responses to determine that a subset of subjects are failing to comply with at least one requirement of the monitoring program. In response, the system may add a subset of new subjects to the monitoring program, or replace the low compliance subjects with a new subset of subjects. Similarly, the management and distribution system may, at an outset of a new monitoring program, modify the requirements of the monitoring program for a particular group of subjects if it determines that one or more requirements are likely to result in one or more group of subjects failing to achieve particular outcomes and, therefore, the monitoring program failing to meet the one or more set goals. As an example, the management and distribution system may use a profile for a group of subjects to determine that a group of subjects is unlikely to have access to a sensor that can obtain data with sufficient accuracy or consistency for the requirements of the monitoring program and, based on this, that the monitoring program is anticipated to fail to obtain viable results. In response to this determination, the management and distribution system may modify the monitoring program for the group of subjects to include additional events for data requests so that additional data can be obtained from the group of subjects during the monitoring program to counter the data inaccuracies or inconsistencies.

This active management and verification is a significant improvement over other systems. For example, even if other systems begin with an appropriate set of subjects, they generally do not have the capability to check on an ongoing basis whether a monitoring program is still viable, or the capability to predict whether the monitoring program will be successful or its results viable by a completion time.

The system also improves the ability of monitoring programs to reach their objectives by the system selecting individuals for monitoring groups that, together, form a monitoring group predicted to meet the defined goals for the monitoring program. In more detail, the system may select a diverse group of subjects to enroll in or invite to the monitoring program such that the composition of the group meets predetermined diversity requirements (e.g., which may be inferred by the system from analysis of other monitoring programs or may be specified by a user). By including a diverse group of subjects at the outset of the monitoring program, the management and distribution system can start the monitoring program in track to obtain a viable monitoring data set from the monitoring program. This is a much needed feature today, as many medical studies today fail to produce viable results or produce results having severely limited applicability due to a failure to include or maintain a diverse set of participants. As discussed below, diversity is not limited to demographic attributes such as age, sex, race, socioeconomic status, and so on, but can also encompass diversity among physical characteristics, medical histories, genetic profiles, geographic locations, and many other attributes that are not demographic in nature.

The disclosed systems may also take into account other attributes of the subjects when selecting a group of subjects to enroll or invite to a monitoring program. For example, the systems may take into account historical data, trends in the historical data, and, optionally, trends among certain populations to select subjects that are likely to meet the requirements of the study. The historical data or trends may indicate past or anticipated retention rates for subjects or groups of subjects, past or anticipated compliance rates for subjects or groups of subjects, or past or anticipated data quality obtained from subjects or groups of subjects. For example, the historical data may indicate that a particular subset of subjects is likely to have low compliance with a particular requirement of a monitoring program. In response to this determination, the management and distribution systems may avoid enrolling or inviting those subjects to the monitoring program. More significantly, to ensure that members of these groups are still represented and participate successfully, the system can increase the number of participants from that group to account for higher expected attrition. More efficiently, the system can identify the elements that are correlated to low compliance for that group, and change those elements or include additional supports to boost compliance, specifically providing supports that the historical data has shown to have been effective in prior monitoring programs with that specific group. For example, those subjects may be needed to achieve certain minimum diversity criteria or other goals for the monitoring program. The system may modify the elements of the monitoring program for that particular subset of subjects to improve compliance. Modifying the elements may include modifying or removing requirements of the monitoring program, or adding remedial elements. For example, if the particular subset of subjects is determined by the management and distribution system to generally not have access to a vehicle and, as a result, have low compliance with required medical office visits, the system may add taxi credit to a new version of the monitoring program for those subjects as a remedial measure to improve compliance rates for those subjects with respect to office visits.

In selecting subjects at an outset of a monitoring program or determining how to modify the elements of a monitoring program to improve, the disclosed systems may use various profiles that represent categories of subjects. These profiles may be used to determine how particular subjects are likely to respond to certain monitoring program requirements, and, therefore, to determine if they should be enrolled to the monitoring program or if the monitoring program needs to be adjusted for one or more particular groups of subjects. These profiles may additionally or alternatively be used to improve the diversity of a monitored group or to determine if a monitored group has a sufficient diversity. For example, the management and distribution system may identify the profiles corresponding to a monitoring group and use the profiles to determine if there is sufficient diversity, at the outset or predicted diversity at completion of the monitoring program. If diversity is insufficient, the management and distribution system may use the profiles to identify unrepresented or underrepresented profiles, and proceed to enroll or invite subjects from categories represented by those unrepresented or underrepresented profiles.

By selecting at the outset of a monitoring program a group of subjects that will likely provide the overall set of data and the diverse context to be able to capture the variety of data needed for the monitoring program, the management and distribution systems are able to significantly reduce computational inefficiencies. Notably, this selection improves the likelihood of obtaining viable results and otherwise successfully completing the monitoring program, which reduces, on average, the time to complete the monitoring programs and/or eliminates, or at least significantly reduces, the need to repeat monitoring programs and the waste resulting from having to cancel or discard results from a monitoring study that fails to achieve participation from the needed categories of subjects.

Other embodiments of these and other aspects disclosed herein include corresponding systems, apparatus, and computer programs encoded on computer storage devices, configured to perform the actions of the methods. A system of one or more computers can be so configured by virtue of software, firmware, hardware, or a combination of them installed on the system that, in operation, cause the system to perform the actions. One or more computer programs can be so configured by virtue having instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features and advantages of the invention will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram that illustrates an example system for assessing and selecting technologies to meet diversity requirements.

FIG. 2 is a diagram that illustrates an example system for performing diversity assessment and action selection.

FIG. 3 is a diagram that illustrates an example system and process for performing diversity assessment and action selection for a new research study.

FIG. 4 is a diagram that illustrates example tables used for diversity assessment and action selection.

FIGS. 5A-5C are diagrams that illustrate example diversity assessment and action selection interfaces.

FIGS. 6A-6B are diagrams that illustrate group predictions for a research study.

FIG. 7 is a flowchart diagram that illustrates an example process for diversity assessment and action selection.

FIGS. 8A-8B are diagrams that illustrate an example system for customizing monitoring programs involving remote devices.

FIGS. 9A-9B are diagrams that illustrate an example system for generating profiles.

FIG. 10 is a diagram that illustrates an example table that indicates impact scores corresponding to different clusters.

FIG. 11 is a diagram that illustrates an example profile.

FIG. 12 is a flowchart diagram that illustrates an example process for customizing monitoring programs involving remote devices.

FIG. 13 is a flowchart diagram that illustrates an example process for customizing monitoring programs involving remote devices.

Like reference numbers and designations in the various drawings indicate like elements.

DETAILED DESCRIPTION

FIG. 1 is a diagram that illustrates an example system 100 for assessing and selecting technologies to meet diversity requirements. Among other uses, the system 100 can be used to identify and/or select devices and/or software to monitor to ensure that certain diversity criteria is being met. Similarly, the system 100 can be used to analyze device and/or software selections made by a user, and perform an action such as generating a recommendation to adjust the analyzed devices if the selected devices and/or software does not meet the diversity criteria or would not be expected to meet the diversity criteria by the end of a monitoring period. For example, if the selections made by the user would result in an insufficient number of devices associated with a particular group from being monitored, the system 100 may generate a warning to send to an administer with a recommendation to add a device from the underrepresented group, remove a device from one of the overrepresented groups, to add a device from the underrepresented group and remove a device form one of the overrepresented groups, or to perform one or more other actions such as to adjust the criteria for making a device and/or software selection.

Monitoring a set of devices or particular software running on a set of devices may include the system 100 collecting data from a distributed group of devices over a network. Data may be collected over a predetermined amount of time, until a goal is achieved, and/or until an event is detected. The frequency of data collection may be predetermined such that data is collected (e.g., requested and/or received) at predetermined intervals. Alternatively, data may be collected as it is produced, in response to certain goals or milestones being met, in response to certain events occurring or being detected, etc.

The system 100 can be used to, for example, identify (e.g., calculate) a target diversity level for a group of devices to be monitored (or a group of devices that are used to run software to be monitored), assess the current diversity of the group of devices (e.g., that have been selected for monitoring), and generate recommendations to reach or approach the target diversity level. The system 100 includes a client device 104 and a computer system 110 that includes functionality to make diversity assessments of devices and/or software selected for monitoring. The computer system 110 may further include functionality to select corresponding actions to perform or recommend in order to achieve the target diversity level.

It may be important to achieve and/or maintain a target diversity level for monitored devices to ensure the viability of data or results obtained during the monitoring period. For example, the computer system 110 may monitor how particular software performs on a variety of devices associated with different groups to determine how the software will perform when publicly released. If however, the monitored devices are not representative of the devices used by the general population, then data or results obtained from the monitoring of the devices may lack utility or otherwise have only limited applicability. For example, the data or results may fail to identify incompatibility between the monitored software and devices associated with a particular group when those devices are not included in the monitoring or are unrepresented (e.g., when compared to the use of those devices in the general population) to the point that statistically significant results regarding those groups of devices cannot be obtained or are sufficiently unlikely to be obtained.

Accordingly, in identifying a target diversity level and working to achieve or maintain the target diversity level, the computer system 110 can obtain improved data or results from the monitoring of multiple devices or software running on those devices. The improved data or results may be more comprehensive in that they correspond to a more diverse device pool.

In addition, in identifying a target diversity level and working to achieve or maintain the target diversity level, the computer system 110 can more efficiently conduct monitoring of multiple devices or software running on those devices. For example, the computer system 110 may determine that based on a reference population of devices (e.g., devices used in target region), the target diversity level should provide that no devices (or a very limited number of devices) that are Group 4 devices should be monitored. This may be based on, for example, the reference population of devices not including any (or a very limited number of) Group 4 devices. As such, the computer system 110 can improve efficiency by limiting the enrollment and monitoring of devices to those devices that are not Group 4 devices. That is, the computer system 110 can limit the enrollment and monitoring of devices to only those devices that will produce useful results (e.g., as may be indicated by the target diversity level). This has the added benefit of reducing computational burden on the computer system 110, reducing the amount of computing resources (e.g., CPU hours, RAM, etc.) that would have been otherwise spent monitoring the unnecessary devices (e.g., Group 4 devices), freeing up computing resources to perform other tasks, etc.

As illustrated in FIG. 1, the computer system 110 has access to a database 112 and also communicates with the client device 104 over a network 150. The computer system 110 can receive data from the client device 104 and can send data to the client device 104 as will be described in more detail below. For example, the computer system 110 can receive monitoring instructions 106 from the client device 104 indicating monitoring criteria and/or criteria for device selection, an indication of devices to select for monitoring, and/or an indication of software to monitor that is to be run on a cohort of devices. The computer system 110 may store the monitoring instructions 106, e.g., the criteria and/or device selections, in the database 112, perform a diversity analysis using the monitoring instructions 106, generate analysis results and/or recommendation(s) for the monitoring program, and/or transmit the analysis results and/or recommendation(s) to the client device 104. The computer system 110 may additionally or alternatively automatically perform one or more actions based on the results of the diversity assessment. For example, the computer system 110 may automatically adjust monitoring parameters, add or remove inclusion criteria for devices, add or remove exclusion criteria for devices, enroll or remove from enrollment one or more devices, etc.

Eligibility criteria such as inclusion criteria which dictates the minimum requirements that devices must meet to be enrolled in the monitoring program and exclusion criteria which dictates which devices must be excluded (e.g., even if they meet the inclusion criteria) may be used to determine which devices to enroll and, therefore, which devices to monitor. Sometimes eligibility criteria can have a detrimental effect on diversity as certain eligibility criterion may disproportionately impact certain groups of devices, e.g., a particular model of device, devices running a particular operating system (OS), devices running a particular OS version, a particular model or series of devices, devices having a particular hardware component, etc.

The client device 104 may be used by an administrator 102 to perform various actions with respect to the monitoring of devices. For example, the administrator 102 can use the client device 104 to create a new monitoring program (e.g., to test new software or a new version of software, such as a particular software module, a new operating system version, etc.), update a monitoring program (e.g., update parameters of a monitoring program, add or remove inclusion criteria for the devices, add or more exclusion criteria for the devices, enroll or remove devices from monitoring, etc.), and/or monitor the devices (e.g., monitor the performance of devices while running particular software, monitor the devices for errors or malfunctions, monitor the devices activity of participants, data collected from participants, review recommendations from the computer system 210, etc.). The client device 204 may be a computing device, such as a desktop computer, a laptop computer, a smartphone, a tablet, a cell phone, etc.

The computer system 110 may monitor a cohort of devices to, for example, test new or existing software. For example, the computer system 110 may, based on the monitoring instructions 106, determine that a new software program, Program A is to be tested on a cohort of devices to determine if minimum performance can be achieved across a variety of devices, to identify errors that are caused or might be caused as a result of running the Program A on a variety of devices such as system crashes, to determine if Program A causes any devices to overheat or experience other malfunctions, etc. The administrator 102 may initiate the monitoring of devices through the client device 104, e.g., in order to determine if new software is ready for release (e.g., to the public, to a particular country, to a particular region, to personnel of a particular business, to a particular group of persons, etc.), to determine if new or existing software is sufficient to meet the needs of a client (e.g., a particular business, a government entity, a particular group of persons, etc.), to determine if the new or existing software meets performance criteria (e.g., minimum loading times, minimum response times such as a render response time or a server response time, maximum battery life drain on underlying device, minimum throughput performance, minimum concurrency performance, maximum load times, latency requirements, maximum error rates, etc.).

The computer system 110 may monitor a cohort of devices to, for example, test the performance of the devices and/or test the hardware components of devices. For example, the computer system 110 may, based on the monitoring instructions 106, determine that devices with the latest CPU B should be tested to identify the performance benefits provided by CPU B. The administrator 102 may initiate the monitoring of devices through the client device 104, e.g., in order to start the monitoring of devices having CPU B during performance testing. The monitoring instructions 106 may indicate, for example, that the inclusion criteria for the monitoring program includes a requirement that all enrolled devices have the CPU B. Based on this, the computer system 110 may select a subset of available devices for performance testing, where each of the selected devices includes the CPU B. The computer system 110 may perform a diversity analysis on the selected subset of devices. The diversity analysis may reveal, for example, that one more additional devices should be included in the performance testing, such as one or more devices from varying groups of devices (e.g., the groups of devices corresponding to particular manufactures, particular hardware components, particular operating systems or other software, etc.).

The computer system 110 may communicate with the client device 104 and various devices, such as devices in a first group of network devices 120 a, in a second group of network devices 120 b, and/or in a third group of network devices 120 c over a network 150. The network 150 can include public and/or private networks and can include the Internet. The network 150 may include wired networks, wireless networks, cellular networks, local area networks, wide area networks, etc.

The devices in the first group of network devices 120 a, the second group of network devices 120 b, and the third group of network devices 120 c may include devices that may optionally be monitored. These candidate devices may be, for example, specifically used for testing, e.g., software and/or performance testing. For example, these devices may be part of a mobile device farm. These devices may include, for example, network-enabled computing devices, such as one or more desktop computers, laptop computers, smartphones, cell phones, tablets, etc.

A reference composition 122 is the group composition for a particular population of devices and is used by the computer system 110 to determine a composition target 124. That is, the reference composition 122 may reflect group diversity for a particular set of devices (e.g., the set of devices used by the general public, used by a particular government agency, used by a particular business, used by a particular group of individuals that meet certain selection criteria such as being recently released or having particular hardware components, etc.). The reference composition 122 may be selected by the administrator 102, or determined by the computer system 110 based on information provided by the administrator 102 (e.g., provided in the monitoring instructions 106). For example, if the administrator 102 provides in the monitoring instructions 106 that a new software module is to be tested for a particular government agency, the computer system 110 may determine that the devices currently in use for the personnel of the government agency should be used as a reference population of devices. Based on this the computer system 110 may determine the reference composition 122 by, for example, accessing data from the database 112 or from remote storage that indicates that 70% of the personnel of the government agency use smartphones running OS A, and 30% of the personnel of the government agency use smartphones running OS B.

The computer system 210 may use the reference composition 122 to determine a composition target 124 for the set (e.g., cohort) of devices to be monitored. For example, the composition target 124 may be determined by removing one or more group of devices from the reference composition 122, by adjusting the reference composition 122 to account for trends (e.g., to estimate a new composition for a population of devices at a future point in time), etc. The composition target 124 may indicate the sought device diversity for monitoring, e.g., at an enrollment stage of monitoring or, more likely, at the conclusion of the conclusion of monitoring. As an example, if a selected set of devices to be monitored for performance over a period of one month is being performed for new devices that have not previously undergone performance tests, the computer system 110 may determine the composition target 124 by limiting the device population of the reference composition 122 to only those devices in the device population that are new models released in the last year and/or are models that have not previously undergone performance tests. The computer system 110 may use the reference composition 122 to identify the different group percentages for the composition target 124. The computer system 110 may similarly determine what devices to enroll in monitoring based on the cohort composition target 224, e.g., in an effort to have an observed/enrolled group composition 126 of devices at the end of the monitoring program match the composition target 124.

The composition target 124 may additionally or alternately indicate a quota that needs to be met for the different groups of devices. For example, the composition target 124 may additionally or alternatively indicate that there needs to be at least two devices from the Group 3 network devices 120 c and at least one device from the Group 1 network devices 120 a.

Continuing the earlier example, if the reference composition 122 provides for 70% Group 3 devices 120 c and 30% Group 1 devices 120 a and trend data (e.g., previously determined by the computer system 110 and stored in the database 112) indicates that there is a trend of a growing population of percent of Group 3 devices 120 c relative to the population of Group 1 devices 120 a, then the computer system 110 may calculate the composition target 124 based on the trend data as 75% Group 3 devices 120 c and 25% Group 1 devices 120 a.

The group composition 126 may represent the current composition of a relevant population of devices to be monitored. For example, at a time of enrollment, the group composition 126 may refer to the composition of a group of devices that have been enrolled by the computer system 110 in the monitoring program. Similarly, during the monitoring program, the group composition 126 may refer to the composition of a group of devices that are still undergoing monitoring. Some devices that were initially enrolled may no longer be monitored at this time due to, for example, errors, crashes, hardware failures, etc. As another example, some devices that were initially enrolled may have been removed by the computer system 110 from enrollment to meet diversity criteria. Accordingly, the computer system 110 may determine the group composition 126 multiple times through the monitoring program, e.g., at fixed time intervals, in response to errors or warnings being detected, when certain monitoring milestones are reached (e.g., tests regarding a particular performance parameter are completed).

The computer system 110 can also be used to automatically determine devices and/or software eligible for monitoring or to enroll in monitoring based on information provided by the administrator 102. For example, the administrator 102 may provide selection criteria that indicates parameters for monitoring (e.g., as part of the monitoring instructions 106), such as a length of time that the monitoring will take place for, a time of day that the monitoring will take place for, frequency of data requests to the monitored devices and/or data transmissions from the monitored devices, inclusion requirements for the devices or software that are used to determine eligibility, exclusion criteria for the devices or software that may dictate the automatic removal of any devices or software that meet one of the exclusion criterion (e.g., even if the devices or software meet the inclusion requirements), etc.

Where the selection criteria has or is predicted by the computer system 110 to have a disproportionately adverse effect on a particular group of devices (e.g., such that it is predicted that the diversity of devices at an end of the monitoring period will be outside of a target diversity device composition), the system 100 may generate a warning and/or a recommendation to adjust the selection criteria. In some cases, instead of generating a recommendation, the system 100 may perform one or more actions automatically.

The devices or software to be monitored may include, for example, computer devices, such as network enabled smartphones, tablet computers, laptop computers, desktop computers, etc. The devices or software may additionally or alternatively include particular software that is running on device, a virtual machine, a container, a network environment, a virtual network environment, etc. For example, the computer system 110 may be used to monitor a particular type of OS software that it would like to test on multiple different devices (e.g., of different manufacturers, models, hardware specifications, CPU types, RAM amounts, etc.). The computer system 110 may receive an indication from the administrator 102 of the software to be monitored and selection criteria for the devices that are to run the software. The computer system 110 may determine a diverse group of devices to run the software, or determine that a group of devices selected by the administrator 102 does not meet diversity criteria (or is unlikely to meet the diversity criteria).

As illustrated in FIG. 1, the overall process of creating and carrying out a monitoring program may be broken down into different stages 132-140. In the first stage 132, the administrator 102 can select through the client device 104 a set of devices (e.g., cohort of devices) to monitor or the computer system 110 can select the set of devices based on the monitoring instructions 106. Continuing the earlier example, the computer system 110 may select three devices from Group 3 devices 120 c and one device from the Group 1 devices 120 a to monitor based on the monitoring instructions 106 and previously determined trend data. In response to the selection of the set of devices that are to be monitored, the computer system 110 may calculate diversity measures 130 to determine if the selected set of devices meets diversity criteria. The diversity measures 130 may include, for example, the current group composition 126 determined using the selected devices. For example, the computer system 110 may determine that group composition is 75% Group 3 devices 120 c and 25% Group 1 devices 120 a as indicated by the key 128. The diversity measures 130 may include the reference composition 122 or an update to the reference composition 122, the composition target 124 or an update to the composition target 124, and/or the results of a comparison of the group composition 126 (or a predicted group composition 126 for the end of the monitoring program) to the composition target 124. The diversity measures 130 may also include the calculation of new trend data or the updating of existing of trend data using new information.

As an example, in generating the diversity measures 130, the computer system 110 may determine, based on trends in one or more devices populations, that Group 2 devices have a fast growing population. Based on this, the computer system 110 may determine that the results of the monitoring program will have limited applicability if Group 2 devices are unrepresented or not included. As such, the computer system 110 may calculate a new composition target 124 that accounts for the growing use of Group 2 devices. For example, the computer system 110 may determine that the composition target 124 should be 20% Group 1 devices 120 a, 40% Group 2 devices 120 b, and 40% Group 3 devices 120 c.

In the second stage 134, the computer system 110 determines that the selected subset (e.g., the initial device selection) does not meet diversity criteria. For example, the computer system 110 may determine that the diversity criteria is not met if the group composition 126 does not include or is predicted not to include by the end of the monitoring program a device from a device group in the composition target 124. Similarly, the computer system 110 may determine that the diversity criteria is not met if the group composition 126 deviates a threshold percent (e.g., for any one group of devices) or predicted to deviate by a threshold percent by the end of the monitoring program from the composition target 124. As another example, the computer system 110 may determine that the diversity criteria is not met if the group composition 126 or the predicted group composition at the end of the monitoring program is not within range (e.g., for any one group of devices) of a target diversity range (e.g., calculated based on the composition target 124).

In an optional third stage 136, the computer system generates a recommendation to meet the diversity criteria. For example, the computer system 110 may generate a recommendation 108 that it transmits to the client device 104. The recommendation may be to add a device from an underrepresented group of devices, remove a device from enrollment that is in an overrepresented group of devices, remove inclusion an inclusion criterion so that more devices may qualify for enrollment, remove an exclusion criterion so that less devices will be rejected from enrollment, etc. For example, if the inclusion criteria indicated that the device had to be one of a set of specific device models, the computer system 110 may recommend that the inclusion criteria be removed (e.g., as it may be overly restrictive for failing to account for new device models). Similarly, continuing the earlier example, the computer system 110 may recommend for two devices from the Group 2 devices 120 b to be added and for one of the Group 1 devices 120 c to be removed.

In an optional fourth stage 138, the computer system 110 receives confirmation from the client device 104 to perform the actions specified in the recommendation 108.

In some cases, the computer system 110 receives instructions to perform one or more different or modified actions compared to the actions in the recommendation 108.

In the fifth stage 140, the computer system 110 selects a new subset of devices to monitor. For example, in response to receiving a confirmation to perform the recommended actions provided in the recommendation 108, the computer system 110 may enroll two devices from the Group 2 devices 120 b as indicated by the key 128. By doing this, the computer system 110 can update the group composition 126 so that the current group composition 126 or a predicted group composition at the end of the monitoring program will match (or be sufficiently close to) the composition target 124 and/or so that a minimum number of Group 2 devices 120 b can be enrolled in the monitoring program. This would indicate that sufficient device diversity is predicted to be met based on the updates to the enrolled devices.

Other changes or modifications to the enrolled devices may be performed at other stages of the monitoring program. These changes or modifications may be made for diversity purposes based on newly determined diversity measures. These changes or modifications may be based on changes to the enrolled devices, a warning generated in response to new data indicating that the composition target 124 is unlikely to be met by the end of the monitoring program (e.g., new trend data, data indicating the a number of devices from a particular group of devices have been unenrolled, etc.), etc.

FIG. 2 is a diagram that illustrates an example system 200 for performing diversity assessment and action selection. Among other uses, the system 200 can be used to assist in the creation and implementation of research studies (e.g., clinical trials, experimental studies, longitudinal studies, correlational studies, case studies, etc.). Specifically, the system 200 can be used to, for example, identify a target diversity level for a research study (e.g., based on one or more goals for the research study), assess the current diversity of the research study, and generate recommendations to reach or approach the target diversity level. The system 200 includes a client device 204 and a computer system 210 that includes functionality to make diversity assessments of one or more research studies and to select corresponding actions to perform or recommend. The numerous benefits discussed above with respect to the computer system 110 are applicable to the computer system 210.

The systems discussed herein, including the computer system 210, can be used to create, manage, adapt, and evaluate monitoring programs in many fields, including manufacturing quality control, environmental monitoring, health research, and many other areas where sampling is used to monitor a subset of a population. In the case of health research in particular, the system can be a multi-tenant system for administering clinical trials through remote devices. The system can create and adapt clinical trials that involve many users remotely collecting user input and sensor data through mobile devices. The system can create and administer studies for various different organizations, allowing the system to efficiently administer many clinical trials concurrently, each with their own separate monitoring groups (e.g., cohorts of participants with associated devices), objectives for monitoring, data collection and reporting procedures, and requirements for diversity in the monitoring groups.

The techniques described can be used to achieve various benefits. For example, by taking into account diversity at the onset and multiple times throughout a clinical study, numerous health benefits can be achieved. Notably, diversity in a research study can be monitored and actions performed in an effort to maintain a target level of diversity such that the results of the study are applicable to typically unrepresented segments of a population. With respect to new medications and treatments, the applicability of these new medication and treatments may be extended such that those belonging to the underrepresented segments can be safely administered the medications/treatments where there otherwise would have been significant doubt due to, for example, there being no data collected for these groups and/or an insufficient amount of data for the results to be statistically relevant for these groups. Similarly, negative interactions and side effects of new medications/treatments for those belonging to the underrepresented segments are more likely to be identified, thereby reducing the risk of injury or death.

As an example, many recent COVID-19 vaccine trials excluded pregnant women due to pregnant women being a high risk group. Unfortunately, by eliminating this group of participants from the vaccine trials, there is insufficient data to determine if many of the vaccines can be safely administered to pregnant women. The disclosed system may identify pregnant women as a group that should be included for a vaccine or medical trial as an underrepresented or excluded group of participants. This may be especially true for medications that are likely to be taken by pregnant women, such as those that are meant to address pregnancy issues, illness that commonly arise during pregnancy, medications that are typically taken by women between the ages 18 and 54, etc. The disclosed system may further account for the increased risks by, for example, making a researcher aware of the increased risks, recommending additional or more comprehensive monitoring during the study for this particular group of participants, recommending a lower dose of the medication or vaccine being tested for this particular group of participants, etc. In this way, important data can be collected on the underrepresented or excluded group of pregnant women while steps can be taken to reduce the risk presented to this group. The collected data may indicate whether it is safe to administer the vaccine to pregnant women, whether there are any side effects unique to pregnant women, whether there are any reactions to the vaccine associated with medications typically taken by pregnant women, etc. Accordingly, in recommending that this group of typically excluded or underrepresented participants be included in the study, the disclosed system can improve the safety afforded to those in the group.

In general, diversity may refer to the variety of groups or attributes represented in a set (e.g., a monitoring group for a monitoring program, a candidate pool, etc.). One way to assess diversity is to assess distribution of members of a monitoring group across different groups, e.g., to determine whether the distribution of members into the different groups is sufficiently similar to the distribution of those groups among a reference population. This population may be a current population at a geographical region or an expected future population, a group that has been invited to participate in a research study, a group (e.g., cohort) that has enrolled in a research study, a group that has enrolled and remains active in a research study, etc. This population may additionally or alternatively be a subset of a larger reference population.

The categories or profiles used as different groups to assess diversity may be defined using one or more attributes, including demographic and non-demographic characteristics. In many cases it is important to obtain diversity not only in demographics, but also in health status, lifestyle, location, context, medical history, and other factors to obtain a broad and effective monitoring. As an example, groups to be assessed during a research study may be defined to have certain combinations of attributes, with one group having a first set of attributes, a second group having a second set of attributes, and so on. The attributes used to define groups can include race, ethnicity, nationality (or nationality of relatives such as parents or grandparents), residence in a certain region (e.g., city, county, state, country, etc.), genomics data (e.g., having a particular gene variant or not having a particular gene variant), state of health (e.g., a body fat percentage of less than 25%), physiological attributes (e.g., weighing more than a threshold amount, weighing less than a threshold amount, having a RHR within a particular range, etc.), psychological attributes, and so on. The attributes can include behavior factors such as sleep, diet, exercise, and other behavior factors.

Other examples of characteristics that can be used to define a group may include ages, age ranges, medical conditions, medicines (e.g., that have been prescribed and/or are being taken by individuals), treatments (e.g., that individuals are undergoing), etc. For example, a first group of a particular population may correspond to all those in the population that have been diagnosed with diabetes, while a second group of the population may correspond to all those in the population that have not been diagnosed with diabetes, and a third group of the population may correspond to all those in the population between the ages of 20-40 regardless of whether they are diagnosed with diabetes

It may be important to achieve and/or maintain a target diversity level for participants in a study to ensure the viability of data or results obtained during the study. For example, the computer system 210 may want to perform a research study to determine the effectiveness and safety of a new pharmaceutical. If however, the study participants are not representative of a reference population that is to use the new pharmaceutical, then data or results obtained from the study may lack utility or otherwise have only limited applicability. For example, if a target group composition cannot be achieved by the end of the study (or the target group composition cannot be achieved within a margin of error), the results of the study may fail to identify side effects of those in groups that are typically excluded from studies due to the higher risk they present (e.g., pregnant women, elderly persons, those with particular diseases or other serious ailments, etc.), of those in groups that are typically less likely to join or complete a study, those in groups that have difficulty meeting the requirements of a study (e.g., due to residential location, access to vehicles, access to computing devices, etc.), etc. Similarly, the data may be insufficient to determine if the medication can be safely administered to those groups.

The computer system 210 may access data corresponding to a particular reference population, such as a list of persons in the reference population. The computer system 210 may filter the reference population to those that have the characteristics corresponding to a particular group, and proceed to sample from the filtered list, e.g., in order to identify a manageable list of persons associated with first group. As will be described in more detail below, the computer system 210 can use this sampled list in a variety of ways. For example, the computer system 210 may simply invite the persons in the sample list. As another example, the computer system 210 may analyze the other characteristics of the persons in the sample list and use these characteristics to identify persons to be invited/enrolled in the study, and/or removed from consideration or enrollment for the study. In this way, the computer system 210 can enroll participants who more accurately represent the larger reference set (e.g., the reference population).

In general, study parameters may include the information that defines the protocol for a study. The study parameters may include, for example, one or more locations where the study is to be conducted, an expected number of participants for the study, a length of the study, an age range of participants for the study, a budget for the study, a target date for the release of a medication or treatment being studied, etc. The study parameters may also include requirements of the participants and/or for how the study is to be conducted. For example, a first parameter for a new research study may provide that participants will be required to attend three medical office visits per month, and a second parameters may provide that participants are required to have or obtain a smartphone. The study parameters may be set by a researcher 202 through the client device 204, by the computer system 210, or by a combination of the researcher 202's inputs and the computer system 210.

In general, eligibility criteria (also referred to as selection criteria) are used to identify devices and users that can appropriately participate in a monitoring study. The criteria may include inclusion criteria and exclusion criteria described in more detail below. Eligibility criteria may also refer to other requirements provided in the study parameters. For example, there may be an eligibility criterion that participants have access to a smartphone, laptop, or other network-enabled computing device due to the study parameters requiring the participants to make frequent telehealth visits. The system 210 ensures not only that eligibility criteria are satisfied for participants in a monitoring program, but that the desired level of diversity is present across the monitoring group as a whole, with diversity being tracked and achieved for each of the different types of attributes that are important for achieving the outcome of the monitoring program.

In general, inclusion criteria may refer to the minimum requirements that candidates must meet in order to be enrolled in study. The inclusion criteria may be based on the question (e.g., goal) of the study, e.g., provided by the researcher 202. For example, if a new study is designed to research the side effects of a new Drug X for the treatment of high cholesterol, then the inclusion criteria for the study may include a requirement that participants have high cholesterol. The inclusion criteria may be set by the researcher 202 using the client device 204. The computer system 210 may additionally or alternatively set the inclusion criteria. For example, based on information sent to the computer system 210 from the client device 204 indicating that the goal of the researcher study is to identify the side effects of a new Drug X for the treatment of high cholesterol, the computer system 210 may, based on this information, add a requirement that each participant have high cholesterol to the inclusion criteria or may generate a recommendation for the researcher 202 to add this criterion or confirm its inclusion. Other examples of inclusion criteria may include a particular age or age range (e.g., corresponding to a particular group of persons that are most likely to take Drug X).

In general, exclusion criteria may refer to attributes of candidates that prevent them from being enrolled in the study (e.g., even if they meet the inclusion criteria). The exclusion criteria may be based on and/or otherwise take into consideration risk factors. For example, the exclusion criteria may prevent those who are pregnant or over a certain age from participating in the study. The exclusion criteria may be based on the question (e.g., goal) of the study, e.g., provided by the researcher 202. For example, if a new study is designed to research the side effects of a new Drug X for the treatment of high cholesterol, then the exclusion criteria may include attributes that have known negative effects with Drug X. For example, the computer system 210 may refer to previous studies stored in a database 212 that indicates that Drug X cannot be safely administered to those with diabetes. In response, the computer system 210 may automatically add diabetes as an exclusion criterion for the research study, or generate a recommendation to add diabetes as an exclusion criterion and send the recommendation to the client device 204 to present to the researcher 202.

As illustrated in FIG. 2, the computer system 210 has access to the database 212 and also communicates with the client device 204 over a network 250. The computer system 210 can receive data from the client device 204 and can send data to the client device 204 as will be described in more detail below. For example, the computer system 210 can receive data from the client device 204 indicating a question and/or requirements for a study (e.g., to initiate a new research study). The computer system 210 may store question and/or requirements in the database 212, perform a diversity analysis using the questions and/or requirements, generate analysis results and/or recommendation(s) for the new study, and/or transmit the analysis results and/or recommendation(s) to the client device 204. The computer system 210 may additionally or alternatively automatically perform one or more actions based on the results of the diversity assessment. For example, the computer system 210 may automatically adjust study parameters, add or remove inclusion criteria, add or remove exclusion criteria, enroll or remove one or more participants, send invitations to enroll to one or more participants, etc.

The client device 204 may be used by a researcher 202 to perform various actions with one or more research studies. For example, the researcher 202 can use the client device 204 to create a research study, update a research study (e.g., update parameters of a research study, add or remove inclusion criteria of a research study, add or more exclusion criteria of a research study, enroll or remove participants of a research study, etc.), and/or monitor a research study (e.g., monitor activity of participants, data collected from participants, review recommendations from the computer system 210, etc.). The client device 204 may be a computing device, such as a desktop computer, a laptop computer, a smartphone, a tablet, a cell phone, etc.

The computer system 210 may communicate with the client device 204 and the participant devices 220 over a network 250. The network 250 can include public and/or private networks and can include the Internet. The network 250 may include wired networks, wireless networks, cellular networks, local area networks, wide area networks, etc.

The computer system 210 can also communicate with participant devices 220. The participant devices 220 may belong to users who have been invited to enroll in a research study, have enrolled in the research study, and/or are enrolled and active in the research study. The participant devices 220 may be computing devices. For example, the participant device 220 may include one or more desktop computers, laptop computers, smartphones, cell phones, tablets, etc.

A reference composition 222 is the group composition for a particular population and is used by the computer system 210 to determine a cohort composition target 224. That is, the reference composition 222 may reflect group diversity for a particular population (e.g., for a particular region, a particular region at a future point in time, those having a particular medical condition, those taking a certain medication, those belonging to a particular age group, etc.). The reference composition 222 may be selected by the researcher 202, or determined by the computer system 210 based on information provided by the researcher 202. For example, if the researcher 202 provides that a study regarding a new cholesterol medication is to take place in Virginia and does not specifically indicate a reference population, the computer system 210 may use Virginia as a reference population. The computer system 210 may proceed to identify the group composition of the reference population. For example, the computer system 210 may look up and/or estimate the percentage of Virginia's population that have or are likely to experience high cholesterol, percentages corresponding to particular races or ethnicities, percentages corresponding to particular age ranges, etc.

The computer system 210 may use the reference composition 222 to determine a cohort composition target 224. For example, the cohort composition target 224 may be determined by removing one or more groups from the reference composition 222 (e.g., if you want to focus the study on only persons suffering from a particular medication), by adjusting the reference composition 222 to account for trends (e.g., to estimate a new composition for a population at a future point in time), etc. The cohort composition target 224 may indicate the sought group diversity for a new research study, e.g., at enrollment or, more likely, at the conclusion of the research study. As an example, if a new research study is to study the effects of a new cholesterol medication, the computer system 210 may determine the cohort composition target 224 by limiting the population of the reference composition 222 to only those in the population that are suffering from high cholesterol or are likely to suffer from high cholesterol. The computer system 210 may use the reference composition 222 to identify the different group percentages for the cohort composition target 224. The computer system 210 may determine what candidates to invite and/or enroll based on the cohort composition target 224, e.g., in an effort to have a group composition 226 at the end of the study match the cohort composition target 224.

As an example, the reference composition 222 may indicate for a corresponding population that a first group corresponding to those of a first ethnicity makes up 55% of the population, that a second group corresponding to a second ethnicity makes up 20% of the population, that a third group corresponding to third ethnicity makes up 15% of the population, and that a fourth group corresponding to those that have or are likely to from high cholesterol makes up 43% of the population. In determining the cohort composition target 224, the computer system 210 may refer to the reference composition 222 to determine percentages for the first three groups when the population is limited to the fourth group. For example, the computer system 210 may use the reference composition 222 to determine that 52% of the individuals in the fourth group also belong to the first group, that 25% of the individuals in the fourth group also belong to the second group, and that 12% of the individuals in the fourth group also belong to the third group. The computer system 210 may proceed to set the cohort composition target 224 to 52% for the first group, 25% for the second group, 12% for the third group, and 11% to one or more other groups.

The group composition 226 may represent the current composition of a relevant population of the study. For example, the group composition 226 may refer to the composition of a group of candidates that were invited to enroll in a study and/or to a cohort of enrolled participants. The computer system 210 may determine the group composition 226 multiple times through the study, e.g., once for each stage of the research study. The group composition 226 may be calculated using different populations. For example, the group composition 226 may initially be calculated for the candidate pool of potential participants, to a group of candidates that were invited to enroll, to the cohort of enrolled participants (e.g., at one or more points throughout the study), etc.

As illustrated in FIG. 2, a research study may be broken down into different stages 232-242. In the first stage 232, the researcher 202 can use the client device 204 to set a research question or study objective. For example, the researcher 202 can initiate a new study through the client device 204 by submitting a study objective to study the side effects of a new Drug X for treating high cholesterol. In the second stage 234, the researcher 202 and/or the computer system 210 set parameters for the study, such as a study size, a region where the study is to take place, devices and/or sensors needed for the study, etc. As an example, the computer system 210 may generate one or more recommendations for study parameters, such as a recommended study size. In the third stage 236, the researcher 202 and/or the computer system 210 set cohort inclusion criteria and/or exclusion criteria. In the fourth stage 238, the researcher 202 and/or the computer system 210 select the cohort. Selecting the cohort may include identifying a group of candidates send enrollment invitation to. Alternatively, selecting the cohort may include identifying a group of candidates from an applicant pool to enroll. In the fifth stage 240, the cohort participants are enrolled. The computer system 210 may generate and send a notification to the participant devices 220 indicating that they have been enrolled. In the sixth stage 242, the study is conducted and the results are monitored. Data may be obtained from the participants through the participant devices 220. The data may be used to identify an activity or participation level of the participants, participants who are inactive, participants who have unenrolled, etc.

Each of the stages 232-242 of the research study may be based on cohort diversity measures 230. That is, the cohort diversity measures 230 may be used by the computer system 210 to confirm or modify how the research study is to proceed from one stage to the next. The cohort diversity measures 230 may include results of one or more diversity analyses, e.g., which may differ depending on the present stage of the research study. For example, with respect to the first stage 232, the cohort diversity measures 230 may include an indication of biases associated with the research question/study objective, or type of research question/study objective. Specifically, if the research study objective is set to study the effects of Drug X for the treatment of high cholesterol, the computer system 210 may access historical data and/or remote data of previous cholesterol research studies that shows that persons of Asian ethnicity are often unrepresented by 20% in cholesterol research studies. There may be relevant reasons for this, such as the possibility of persons of Asian ethnicity being significantly less likely to have high cholesterol. However, despite this, it may be critical to accurately represent the Asian ethnicity segment of the cohort composition target 224 to ultimately determine if Drug X can be safely administered to those of Asian ethnicity and/or to identify dangerous side effects which may disproportionately affect those of Asian ethnicity. More generally, the cohort diversity measures 230 may show that persons of a certain group are consistently underrepresented in clinical trials involving a new medication, such as pregnant persons, persons older than 70 years of age, persons with immune system disorders, etc.

The cohort diversity measures 230 may include one or more diversity levels calculated by the computer system 210. These diversity levels may correspond to a current level of diversity, e.g., present among previous participants (e.g., potential candidates), those invited to enroll in the study (e.g., candidates), those that have accepted to be enrolled in the study and/or are enrolled in the study (e.g., participants), etc. Additionally or alternatively, these diversity levels may correspond to a predicted level of diversity at a future point in time, such as the study completion.

A determined diversity level may indicate how close the predicated group composition at study completion is to the cohort composition target 224. The diversity level may be expressed as, for example, a diversity score. That is, the diversity level may be expressed a value (e.g., a number, a classification, etc.) that is indicative of how close the predicted group composition at study completion is to the cohort composition target 224 (e.g., a target group composition). In some cases, the score can indicate a magnitude of how far the distribution or composition of members of the cohort varies from the target distribution or composition. As an example, a diversity score of 1.0 may indicate that the predicted group composition at study completion matches the cohort composition target 224. Lower scores can indicate increasing differences from the target level, e.g., a score of 0.9 may indicate that the composition varies from the target by at least 5% in at least one category or attribute, a score of 0.8 may indicate that the composition varies from the target by at least 10% in one target or attribute, etc. The scoring can change linearly or nonlinearly with the amount of deviation from the target.

The diversity score for a monitoring group may be determined as an aggregate or composite of separate scores for different categories or profiles for which composition is tracked. For example, if there are 5 different types of participants needed, and only four of the 5 meet the requirements for the minimum number of participants, then the diversity score can be 80%. Separate scores may be determined for each category or group to be included, and those scores can be averaged (e.g., if group 1 has 100% of needed members, group 2 has 90% of needed members, and group 3 has 60% of needed members, the average of 83% can be used as a diversity score for the monitoring group as a whole).

The diversity score may be based on absolute measures, such as the numbers of participants in each group, or it may be relative measures, such as the amount in one group relative to the amount in another category or to the monitoring group as a whole (e.g., a ratio, proportion, fraction, percentage, etc.). A diversity score can also be determined relative to other references, such as a previously predicted group composition, a previously determined diversity score, a predicted group composition corresponding to one or more recommendations (e.g., predicated based on an assumption that the recommended actions will be performed), etc.

Diversity scores can be generated and provided for each of the different categories or attributes that are relevant to the monitoring program. For example, the system can determine, for each of various groups to be represented in the monitoring group, how close the subset representing that group is to the target level for the group. This can help indicate, for example, the specific groups or categories of devices and users where additional representation is needed in the monitoring group.

Additionally or alternatively, the diversity level may be expressed as a probability or confidence score indicating the expected results for the study, such as a likelihood that a minimum amounts or proportions of the different groups represented in the monitoring group will achieve compliance with the requirements until the end of the monitoring program. Because there are multiple different groups or categories of members in the monitoring group, set of probabilities or confidence scores can be determined, with one for each of the different groups or categories. In addition, multiple versions of the scores can be determined for different scenarios, e.g., one for the current state of the monitoring program and the current monitoring group, and others representing the expected likelihood(s) of success that would result after performing different actions corresponding to different recommendations.

The diversity level may indicate a level of confidence in achieving the cohort composition target 224, and/or achieving a group composition that is with an acceptable range (e.g., percent range or value range) of the cohort composition target 224. For example, a diversity score of 0.91 may indicate that the computer system 210 has determined that there is 91% possibility of the group composition at study completion being within a threshold percentage (e.g., 5%, 3%, 1%, etc.) of the cohort composition target 224. Or, if the cohort composition target 224 is expressed as one or more ranges, the score can indicate the likelihood of the composition having representation of groups that falling within the target ranges.

Diversity level may also or alternatively describe a group composition (e.g., a predicted group composition), or the difference between a group composition (e.g., current or predicted) and the cohort composition target 224. For example, a predicted group composition at study enrollment may be a first diversity level, a predicted group composition at study completion may be a second diversity level, and a difference (e.g., difference between two sets of values, absolute value of the difference between the two sets of values, etc.) the group composition at study completion and the cohort composition target 224 as a third diversity level.

In some cases, there are multiple diversity metrics used to assess the level of diversity. For example, a first diversity level may include a diversity distribution indicating different likelihoods of achieving the cohort composition target 224, and a diversity score may be second diversity level identified from the diversity distribution (e.g., as the value associated with the highest probability out of the all of the values).

The target diversity level described above and elsewhere may refer to a single diversity level or to a group of multiple diversity levels or metrics. For example, a target diversity level may require as a first level or metric a requirement that a cohort (e.g., monitored group) meets certain enrollment minimums (e.g., at least one subject from each diversity group). For example, the enrollment minimums may provide that for each medical condition in a list of particular medical conditions, there are at least two corresponding subjects that have the medical condition. The target diversity level may further require a diversity score metric. For example, the monitoring program may require a diversity score of 0.7 or greater by the end of the monitoring program. This diversity score may be calculated using a target group composition of subjects and an observed or anticipated (e.g., at the end of the monitoring program) composition of subjects (e.g., determined from the current or anticipated enrollment of subjects in the monitoring program). This diversity score can, for example, represent the difference between the actual (e.g., observed or anticipated) composition of subjects and the target composition (e.g., a composition having sufficient or ideal diversity). For example, the diversity score may be a value between 0 and 1. Here, 0 may correspond to complete or maximum divergence or difference between a vector (or array) representing an actual composition of subjects and a second vector (or array) representing the target composition of subjects. Similarly, 1 may correspond to no or minimum divergence or difference between the two vectors or the two arrays. The computer system 210 may, therefore, calculate a diversity score and/or a target diversity level by calculating the difference or divergence between two or more vectors. Similarly, the computer system 210 may, therefore, calculate a diversity score and/or a target diversity level by calculating the difference or divergence between two or more arrays.

In determining a diversity level such as a diversity score, the computer system 210 may sample from a larger reference set to obtain a makeup that is representative of the larger reference set (e.g., replicating, within a predetermined tolerance, the distribution of certain attributes or characteristics that are relevant to the monitoring program). That is, the computer system 210 may sample from a larger reference set so that the sample reflects the characteristics across the reference group. For example, the computer system 210 may access from the database 212 or from an external data storage, data that corresponds to persons having characteristics that define a first group, such as being of a specific race, being of a specific ethnicity, being of a specific nationality, living in a certain region (e.g., city, county, state, country, etc.), having particular genomics data (e.g., particular gene variant), having a particular state of health, having particular physiological attributes (e.g., weighing more than a threshold amount, weighing less than a threshold amount, having a RHR within a particular range, etc.), having a particular diet or having particular eating habits (e.g., vegetarian, vegan, etc.), having a particular occupation, having a particular level of education (e.g., high school diploma, two years of college, four years of college, graduate degree, etc.), etc. The computer system 210 may access this data, such as a list of persons in the reference population (e.g., used to determine the reference composition 222) that belong to this first group, and proceed to sample the data (e.g., in order to identify a manageable list of persons associated with first group).

The computer system 210 can use this sampled list in a variety of ways. For example, the computer system 210 may simply invite the persons in the sample list, or a subset of persons in the sampled list, to participate in the study. As another example, the computer system 210 may analyze the other characteristics of the persons in the sample list and use these characteristics to identify persons to be invited/enrolled in the study, and/or removed from consideration or enrollment for the study. For example, if the sample data indicates that 95% of users associated with Group 1 also have characteristic B and none of the users associated with Group 1 have characteristic C, the computer system 210 may set characteristic B as inclusion criteria for Group 1 participants, and characteristic C as exclusion criteria for Group 1 participants. In this way, the computer system 210 can enroll participants who more accurately represent the larger reference set (e.g., the reference population).

The computer system 210 may make various recommendations or take various actions based on the determined diversity level(s). These diversity measures may be, for example, compared by the computer system 210 to one or more thresholds that correspond to particular recommendations and/or actions.

As another example with respect to the first stage 232, the cohort diversity measures 230 may also include an indications of determinations made by the computer system 210 with respect to whether the question/objective is too limited. For example, the computer system 210 may generate a warning if it determines that a study objective set by the researcher 202 will result in a restrictive FDA label. The computer system 210 may also generate a recommendation or identify an action to perform as part of the cohort diversity measures 230. For example, if the objective is to study the effects of a particular treatment on a small population segment, the computer system 210 may generate a recommendation to modify the objective to take into account other population segments, such as similar population segments.

As another example, with respect to the second stage 234, the cohort diversity measures 230 may include indications of particular study parameters that disproportionately affect certain groups of the cohort composition target 224. For example, the computer system 210 may generate a warning for a study parameter that requires that participants have or use a smartphone upon determining that the study parameter is predicated to significantly reduce the enrollment of users belonging to an example Group A. The cohort diversity measures 230 may also include a recommendation for a revised study parameter, a replacement study parameter, a removal of the study parameter, or an addition of a study parameter (e.g., the providing a smartphone to users in Group A as part of the study). The recommendations may be specific to particular population groups. Instead of a recommendation, one or more of the actions may be automatically performed by the computer system 210. The cohort diversity measures 230 may additionally or alternatively include the reference composition 222 determined by the computer system 210 based on the study parameters.

As another example, with respect to the third stage 236, the cohort diversity measures 230 may include indications of particular study criteria that disproportionately affect certain groups of the cohort composition target 224. For example, the computer system 210 may generate a warning for an exclusion criterion that prevents those older than 65 years old from enrolling in the study upon determining that the majority of persons belonging to Group B that are likely to enroll are older than 65 years old and/or upon determining that persons belonging to Group B that are older than 65 years old are much more likely to be retained than those in Group B under 65 years old. Accordingly, the exclusion criterion may prevent or make it exceedingly difficult for the group composition 226 to reach the cohort composition target 224 by the end of the research study. The cohort diversity measures 230 may also include a recommendation for a revised inclusion criterion or exclusion criterion, a replacement inclusion criterion or exclusion criterion, a removal of an inclusion criterion or exclusion criterion, or an addition of an inclusion criterion or exclusion criterion. The recommendation may be specific to particular population groups. Continuing the earlier example, the computer system 210 may generate a recommendation to remove the age exclusion criterion for only those belonging to Group B (e.g., to account for the particular conduct of Group B participants while limiting the amount of risk introduced that comes from enrolling those of advanced age). The cohort diversity measures 230 may additionally or alternatively include the reference composition 222 determined by the computer system 210 based on the study parameters, the inclusion criteria, and/or the exclusion criteria.

As another example, with respect to the fourth stage 238, the cohort diversity measures 230 may include recommendations as to what candidates should be invited for enrollment in the study, and/or what candidates should be accepted for enrollment if there are a group of applicant candidates. The cohort diversity measures 230 may also include a determined group composition for the recommended invitees/applicants, for the predicted cohort of enrolled participants at the start of the study (e.g., based on a prediction of what percentage of invitees from the different groups are expected to enroll), and for the predicted cohort of enrolled participants at the end of the study (e.g., based on a prediction of what percentage of the invitees from the different groups are expected to enroll and expected to be retained/remain active). The recommendation may be specific to particular population groups. As an example, the computer system 210 may generate a recommendation to invite 25% more candidates from Group B than from Group A based on historical data indicating that those from Group A are 20% more likely to enroll than those from Group B. Instead of generating a recommendation to send to the client device 204, the computer system 210 may perform one or more actions automatically. For example, the computer system 210 may automatically determine which candidates from an applicant pool to enroll, and/or which candidates from a candidate pool (e.g., previous study participants, previous study applicants, previous study participants that completed their respective studies, etc.) to invite.

As another example, with respect to the fifth stage 240, the cohort diversity measures 230 may include an updated group composition 226 based on the enrolled participants and an updated prediction for the group composition 226 at the end of the study (e.g., based on the past behaviors and/or trends of the different groups). The cohort diversity measures 230 may include an indication of the results of a comparison between the updated prediction for the group composition 226 at the end of the study and the cohort composition target 224. For example, the cohort diversity measures 230 may include warning that indicates too many persons from Group A have enrolled relative to the number of persons from Groups B and C that have enrolled. The cohort diversity measures 230 may also include recommendations corresponding to the enrolled participants. For example, if more candidates from Group A enrolled than was anticipated, the recommendations may include removing one or more of the participants from Group A (e.g., those that have been identified as the least reliable/active from historical data and/or stored user profile information in the database 212), to send new invites to candidates of Groups B and C, and/or to revise the study parameters, the inclusion criteria, and/or the exclusion criteria in order to increase the retention of those in Groups B and C. As an example, due to a low enrollment of Group C participants and trend of poor retention of Group C participants when they are required to visit medical offices, the computer system 210 may determine as a recommendation a support option to account for the identified problem. For example, the computer system 210 may determine a first recommendation to provide Group C participants taxi fare to counter the known cause of the poor Group C retention, and a second recommendation to allow Group C participants to make their appointments through telehealth services.

In some cases, the fourth stage 238 and the fifth stage 240 are part of the same stage. For example, if there are pool of candidates who have applied, selecting the cohort from the applicant pool may include enrolling those selected as participants for the study.

As another example, with respect to the sixth stage 242, the cohort diversity measures 230 may include an updated group composition 226 based on the remaining enrolled participants and an updated prediction for the group composition 226 at the end of the study. For example, the computer system 210 can take into account participants that have left the study or that are no longer active. The cohort diversity measures 230 may include an indication of the results of a comparison between the updated prediction for the group composition 226 at the end of the study and the cohort composition target 224. For example, the cohort diversity measures 230 may include warning that indicates too many persons from Group B are at risk of being removed from the study due to low activity, and/or that it is unlikely that the cohort composition target 224 can be reached based on observed data for Group B and previously determined trends for Group B. The cohort diversity measures 230 may also include recommendations corresponding to the enrolled participants. For example, the computer system 210 may recommend adding one or more study parameters specific to participants in Group B that have previously worked to increase participation.

Many different research studies are conducted every year, including clinical studies for new treatments and medications. However, health care disparities are an issue plaguing various research studies that can arise due to a failure to take into account certain segments of a population. Particular segments of the population, such as older adults, pregnant women, children, and racial and ethnic minorities are affected in different ways but are often underrepresented in research studies. As a consequence, the results of these research studies may have limited applicability, particularly for those in the underrepresented segments of the population. This often leads to health care disparities such that there is incomplete or unreliable information as to how those segments of the population will be affected, which can prevent, for example, new medications and treatments from being safely administered to those segments of the population. Moreover, many current studies fail to take into account behavioral variation among the different segments of the population. This often leads to lower enrollment and retention of some segments of the population.

The techniques discussed herein enable the computer system 210 to detect and correct for bias and underrepresentation of different population segments at many stages throughout the research study process. As noted above, the computer system 210 can calculate diversity metrics and impacts of different factors in a study on different segments, when defining a research question (232), when setting study parameters (234), when setting cohort inclusion and exclusion criteria (236), when selecting members of cohorts (238), enrolling participants (240), and when conducting a research study and monitoring results (242). At each stage, the computer system 210 can assess levels of representation to verify that the needed diversity is present both for the current study data and cohort, as well as for the ultimate outcomes for the study (e.g., for the group within the cohort that is retained to completion of the study, and the data set obtained by the end of the study). At each stage, the computer system 210 can assess diversity and alert a study administrator if the composition deviates from a target composition by more than a predetermined amount (e.g., exceeds a threshold level of difference for at least one segment of interest). The computer system 210 can also identify and recommend actions, e.g., changes to the study parameters or group of individuals selected as a cohort that will improve the distribution or composition of the cohort toward target levels representing the desired level of diversity.

FIG. 3 is a diagram that illustrates one or more components of the system 200 and a process for performing diversity assessment and action selection for a new research study.

In general, FIG. 3 illustrates the researcher 202 initiating a research study through the client device 204. The computer system 210 can use the information received from the client device 204 to calculate one or more diversity measures, such as the reference population for the study, the reference composition, and the target composition. The computer system 210 may also invite users to enroll in the study and/or enroll users in the study based on the received information. Based on the users that enrolled and their associated groups, the computer system 210 may generate and transmit to the client device 204 a recommendation to improve the diversity of the study participants.

FIG. 3 illustrates various operations and flows of data represented as stages (A)-(E), which can be performed in the order shown or in a different order. For example, one or more of the stages (A)-(E) can occur concurrently.

As illustrated, in stage (A), the researcher 202 may be presented an interface 304 a of the client device 204 to create a new research study and/or to set a new research question. The researcher 202 may interact with one or more interface elements in the interface 304 a to set a study question, to set study parameters, to set inclusion criteria, and/or to set exclusion criteria.

In stage (B), after providing the study question and/or the corresponding study parameters, the researcher 202 may submit the new study to the computer system 210. For example, in response to receiving an indication that the researcher 202 has selected a “Submit” interface element, the client device 204 may generate and transmit study data 306 to the computer system 210 over the network 250.

The study data 306 may include, for example, a study question or research objective, study parameters, inclusion criteria, and/or exclusion criteria. In some cases, the study data 306 may only include the study question or research objective. The computer system 210 may automatically select the study parameters, inclusion criteria, and/or exclusion criteria based on the study question/research objective and/or based on default values. Alternatively, the researcher 202 may set the study parameters, the inclusion criteria, and/or the exclusion criteria at a later time, e.g., after the computer system 210 has performed a diversity analysis of the study question or research objective.

In stage (C), in response to receiving the study data 306, the computer system 210 may perform a diversity analysis using the study data 306. In performing the diversity analysis, the computer system 210 may calculate the cohort diversity measures 230 discussed above with respect to FIG. 2. The computer system 210 may, for example, use a diversity analysis module 310 to perform the diversity analysis based on the study data 306 and/or device data 322, to generate recommendations based on the diversity analysis, and/or to perform one or more actions based on the diversity analysis. The device data 322 may include data received from or generated for Group 1 devices 320 a that correspond to a first group of users (e.g., candidates or enrolled participants), Group 2 devices 320 b that correspond to a second group of users (e.g., candidates or enrolled participants), and/or Group 3 devices 320 c that correspond to a third group of users (e.g., candidates or enrolled participants). The Group 1 devices 320 a, the Group 2 devices 320 b, and the Group 3 devices 320 c may represent three groups of devices whose corresponding users are candidate participants for the study. As an example, these devices may represent all users (e.g., previous study participants; previous study applications; current study applications; etc.) who meet the inclusion criteria and do not meet the exclusion criteria. Similarly, these devices may represent users who the computer system 210 has invited to enroll in the study. As another example, these devices may represent the users who have enrolled in the study.

The device data 322 may indicate, for example, data received from at least a subset of the Group 1 devices 320 a, the Group 2 devices 320 b, and/or the Group 3 devices 320 c. For example, the device data 322 may include response to invitations to enroll in the study. The device data 322 may, therefore, indicate each of the users that have been invited to enroll in the study and have agreed to enroll in the study. Similarly, the device data 322 may include data indicating which users have enrolled in the study. The device data 322 may also or alternatively indicate other information such as which invitations to enroll have successfully been transmitted to the users (e.g., to their corresponding device), which users have viewed an invitation to enroll (e.g., but have not yet responded), the percent of users by group that have responded positively to an invitation to enroll, the percent of users by group that have responded negatively to an invitation to enroll, etc.

As part of the diversity analysis, the diversity analysis module 310 of the computer system 210 may perform an operation 312 of calculating a composition to use for the study based on the study data 306 and/or the device data 322. The composition may be current group composition or a predicted composition at a future point in time, such as at the end of the study (e.g., determined based on machine learning models or statistical data indicating the predicted compliance or retention levels for different groups). The composition may be, for example, the group composition 226 shown in FIG. 2. As an example, the diversity analysis module 310 may use the device data 322 indicating which devices (and their corresponding users) have responded positively to an invitation to enroll in the study. That is, the device data 322 may indicate which users have enrolled in the study. The diversity analysis module 310 may use the device data 322 to determine the current group composition (e.g., participants who are currently enrolled), or a predicted group composition (e.g., at the time of enrollment, or at the time of study completion).

In some cases, the diversity analysis module 310 may use the device data 322 to update the database 212. For example, the diversity analysis module 310 may simply store the device data 322 on the database 212. As another example, as explained in more detail below with respect to FIG. 4 and FIGS. 6A-6B, the diversity analysis module 310 may use the device data 322 to update trend data associated with different user groups. This trend data may be stored in the database 212, and used by the diversity analysis module to make predictions and/or recommendations. For example, the diversity analysis module 310 may update trend data in the database 212 corresponding to the likelihood of a Group 1 users accepting invitations to enroll in a study based on the number of invitations successfully transmitted to the Group 1 devices 320 a and the number of positive responses received from the Group 1 devices 320 a as provided in the device data 322.

The diversity analysis module 310 performs a second operation 314 of comparing the group composition to a composition target, such as the cohort composition target 224 shown in FIG. 2. The composition target may be set by the researcher 202 and be part of the study data 306. Alternatively, the computer system 210 may determine the cohort composition target based on the study data 306. For example, the computer system 210 may first determine a reference composition based on the study data 306. As an example, the computer system 210 may use the region parameter (Virginia) and the target date parameter (Q2 2025) to determine the reference composition. Specifically, the computer system 110 may use the two parameters and corresponding data stored in the database (e.g., trend data, population data, etc.) to estimate the population composition in the region at the target date (e.g., the ethnic population composition of Virginia in the year 2025). The computer system 110 may proceed to set this estimated population composition as the reference composition.

From the reference composition, the diversity analysis module 310 may determine the cohort composition target. For example, the diversity analysis module 310 may use the inclusion criteria of must be at least 30 and the exclusion criteria of cant be pregnant to limit the population of the reference composition to only those who are over 30 and/or are not pregnant. The diversity analysis module 310 may calculate the cohort composition target 224 from the reference composition using the identified subset of the population. In some cases, the diversity analysis module 310 may detect diversity issues, such as that the inclusion criteria and/or the exclusion criteria are too limiting (e.g., to specific groups). For example, the diversity analysis module 310 of the computer system 210 may generate a warning if the updated population to use for the cohort composition target 224 excludes or significantly reduces the representation of certain segments in the reference population.

In comparing the group composition to the composition target, the diversity analysis module 310 may determine if the group composition matches the composition target, is within a threshold percent (e.g., for any one group) of the composition target, and/or falls within an acceptable target diversity range (e.g., calculated based on the composition target).

The diversity analysis module 310 performs a third operation 316 of generating a recommendation to send to the client device 204 based on the results of the comparison. In generating a recommendation, the diversity analysis module 310 may perform a statistical analysis to identify one or more actions to include in the recommendation. The statistical analysis may be based on identified correlations between different parameters, criteria, and/or actions and certain participant groups and/or group trends. For example, historical data may indicate that past participants associated with Group C have a very low percentage of attending medical appointments, and that previously performed actions of providing taxi credits was typically effective at improving the visit attendance of participants in this group. Accordingly, if the diversity analysis module 310 identifies an issue where participants of Group C are not attending medical appointments, the computer system 210 may recommend that the researcher 202 provide the Group C participants with a taxi credit. The computer system 210 may also make this recommendation prior to any attendance issue being observed, to prevent such an issue from arising.

Additionally or alternatively, in generating the recommendation, the diversity analysis module 310 may use one or more machine learning models to select one or more actions to recommend. As an example, the diversity analysis module 310 may provide an input vector describing the characteristics of the study, such as the cohort composition target, as input to a machine learning model. The diversity analysis module 310 may additionally or alternatively provide a second input vector describing the current characteristics of the cohort, such as the group composition 226, as input to the machine learning model. The output of the machine learning model may be a vector containing a series of values. Each value may correspond to a particular action such that a relatively higher value indicates that a particular action corresponding to the value is better suited for recommendation and/or performance given the input data.

As an example, the diversity analysis module 310 may determine that the exclusion criterion of preventing those who are pregnant from joining the study will be too detrimental to certain population groups. If reference population of the calculated composition may correspond to persons who are likely to take “Drug A” for the treatment of a certain condition and the diversity analysis module 310 determines that a large percentage of persons who are anticipated to take Drug A are pregnant women, then the diversity analysis module 310 may generate update the cohort composition target to include pregnant women as a first group. The diversity analysis module 310 may proceed to generate a diversity warning based on a comparison of the group composition containing no pregnant women to the updated cohort composition target that now includes pregnant women as a group. In response to the warning, the diversity analysis module 310 may provide an input vector describing the characteristics of the study, the updated cohort composition target, and the group composition to a set of machine learning models. The output of the machine learning models may indicate one or more recommended actions to perform in order to achieve the cohort composition target. For example, the output of the machine learning models may correspond to a an action of removing the exclusion criterion of preventing pregnant women from joining the study, as this has the most significant and detrimental effect on the group composition 226 with respect to pregnant women and prevents the cohort composition target from being achieved. Be removing this exclusion criterion, pregnant women will be permitted to join the research study which may allow the cohort composition target that includes pregnant women to be achieved.

The diversity analysis module 310 may also calculate metrics corresponding to a recommendation. For example, the diversity analysis module 310 may calculate the potential effects of the recommendation on group enrollment (e.g., likelihood of enrollment), of group composition (e.g., at start of study, at the end of the study, etc.), on the likelihood of reaching the cohort composition target, etc.

In stage (D), the diversity analysis module 310 performs a fourth operation 318 of generating instructions for the computer system 210 to send the recommendation and corresponding metrics to the client device 204 over the network 250. The metrics may also include the calculated reference composition, the cohort composition target, a current group composition, and/or a predicated group composition at a future point in time (e.g., at the end of the study).

In stage (E), the client device 204 presents a recommendation interface 304 b to the researcher 202. The recommendation interface 304 b may depict the diversity metrics such as the cohort/group composition and the cohort composition target. The recommendation interface 304 b may also present one or more recommendations, and their corresponding effects on cohort diversity. If multiple recommendations are presented, the recommendations may be presented in an order corresponding to what actions or groups of actions are most likely to succeed in reaching the cohort composition target 224, or most likely to get sufficiently close to the cohort composition target 224.

As discussed in some detail above, the diversity analysis module 310 may use one or more algorithms to perform the diversity analysis, generate the diversity measures 130, and/or generate recommendations. For example, the diversity analysis module 310 may use one or more static algorithms to calculate the group composition at a future point in time using previously obtained data or previously determined trends (e.g., generated using previously obtained data) for those groups of users (e.g., indicating the expected or average percentage of users in a particular group completing a study). The diversity analysis module 310 may additionally or alternatively use one or more machine learning algorithms trained to predict the group composition at a future point in time, and/or trained to select actions to perform and/or predict the diversity effects of those actions. The one or more machine learning algorithms may be trained using historical data that indicates the behavior of past study participants (e.g., an indication as to whether they completed or unenrolled from a study, an activity level of the participant during the study, etc.), the groups that the participants belong to, and/or the study parameters (e.g., inclusion criteria, exclusion criteria, other requirements or study parameters).

As input (e.g., an input vector), the one or more machine learning algorithms may receive an indication of the participants currently enrolled in the study and/or the group composition (e.g., group composition 226), and the current study parameters (e.g., including inclusion criteria and exclusion criteria). Other input may include an indication of the observed activity levels of users and/or groups for this study, and/or an enrollment trend of users and/or groups for this study.

With respect to machine learning algorithms configured to predict the group composition at the end of the study, the output (e.g., an output vector) of the machine learning algorithm may include a vector of values that correspond to different group composition possibilities. The highest value may indicate the most likely group composition that will be observed at the end of the study.

With respect to machine learning algorithms configured to select actions and/or predict the effects of actions, the output (e.g., an output vector) of the machine learning algorithms may include a vector of values corresponding to different actions and/or different combinations of actions. The highest value may indicate the highest recommended action or combination of actions, corresponding to the action or combination of actions that is most likely to result in achieving the cohort composition target, getting sufficiently close to the cohort composition target, and/or getting closer to the cohort composition target than other actions or combinations of actions.

FIG. 4 is a diagram that illustrates example tables 402-410 used for diversity assessment and action selection. The tables 402-410 may be generated, updated, and referenced by the computer system 210. The computer system 210 may use the information in the tables 402-410 to perform a diversity analysis, calculate diversity measures (e.g., including diversity levels), and generate recommendations. The computer system 210 may store the tables 402-410 in the database 212.

Importantly, the tables 402-410 indicate the different effects that different monitoring program parameters have on the actions of individuals in different groups. For example, one element, such as a requirement of an in-person visit, may have a high negative effect on enrollment or compliance of members of one group but may have a less negative effect, or a neutral or positive effect on enrollment or compliance of members of another group. The computer system 210 analyzes the records of prior actions to characterize or profile the correlations between different factors and the resulting observed outcomes. This allows the system to quantify the potential bias that different factors cause on enrollment, compliance, and retention. It also provides the source data that the system 210 can use to trace the causes of low enrollment, compliance and retention in monitoring programs and signals to the system 210 the opportunities to address factors that may be harming monitoring program outcomes. For example, the system 210 can detect that compliance among a particular group is low, identify from the tables a data collection action that is correlated with low compliance, and then select an alternative method of collecting the data that the tables indicate has a higher likelihood of compliance

As an example, the tables 402-410 may be generated by the computer system 210 using historical data 412 and study/cohort data 414 (e.g., data collected from study participants, diversity analyses during a study, etc.). The tables 402-410 may represent determined trend data 416 and/or predictions/recommendations 418 corresponding to different participant groups.

As illustrated, different groups of participants may be associated with different behaviors during a research study, and/or are likely to react differently to particular actions, study parameters, inclusion criteria, and/or exclusion criteria. By identifying how different groups are likely to behave and/or react, a study can be generated and conducted in a manner to ensure (or significantly increase the likelihood) of achieving a set cohort composition target by the end of the study.

A first table 402 indicates example trends in activities levels and study completion rates for different groups. The computer system 210 may generate the first table 402 using historical data 412. The historical data 412 may include, for example, enrollment data from one or more past studies, participation data from one or more past studies, participant profiles corresponding to participants in one or more past studies, etc. The participant profiles may include data that indicates one or more groups associated with the participants, such as demographic and/or non-demographic information of the participants. The participant profiles may include enrollment data (e.g., an indication of how many studies that the participant has been invited to join, has applied for, has enrolled in, etc.) and/or participation data (e.g., average participation level of the participant, how often the participant remained an active participant, etc.). The computer system 210 may update the table 402 using study/cohort data, which may include, for example, data measured, observed, or received during a research study such as one currently being conducted.

The first table 402 may be part of trend data 416 that is generated by the computer system 210 based on the historical data 412 and/or the study/cohort data 414. The computer system 210 may refer to the table 402 in order to generate predictions and/or recommendations 418.

A second table 404 indicates the predicted effects of particular study parameters on different groups. The effects may include, for example, anticipated changes to study enrollment, participation, and/or completion likelihood for each group. The second table 404 may be generated by the computer system 210 using the historical data 412. The second table 404 may be updated by the computer system 210 using the study/cohort data 414.

The second table 404 may be part of trend data 416 that is generated by the computer system 210 based on the historical data 412 and/or the study/cohort data 414. The computer system 210 may refer to the table 404 in order to generate the predictions and/or recommendations 418.

A third table 406 indicates the predicted effects of particular inclusion criteria on different groups. The effects may include, for example, anticipated changes to study enrollment, participation, and/or completion likelihood for each group. The third table 406 may be generated by the computer system 210 using the historical data 412. The third table 406 may be updated by the computer system 210 using the study/cohort data 414.

The third table 406 may be part of trend data 416 that is generated by the computer system 210 based on the historical data 412 and/or the study/cohort data 414. The computer system 210 may refer to the third table 406 in order to generate the predictions and/or recommendations 418.

A fourth table 408 indicates the predicted effects of particular exclusion criteria on different groups. The effects may include, for example, anticipated changes to study enrollment, participation, and/or completion likelihood for each group. The fourth table 408 may be generated by the computer system 210 using the historical data 412. The fourth table 408 may be updated by the computer system 210 using the study/cohort data 414.

The fourth table 408 may be part of trend data 416 that is generated by the computer system 210 based on the historical data 412 and/or the study/cohort data 414. The computer system 210 may refer to the fourth table 408 in order to generate the predictions and/or recommendations 418.

A fifth table 410 indicates the predicted effects of particular study actions on different groups. The effects may include, for example, anticipated changes to study enrollment, participation, and/or completion likelihood for each group. The fifth table 410 may be generated by the computer system 210 using the historical data 412. The fourth table 408 may be updated by the computer system 210 using the study/cohort data 414.

The fifth table 410 may be part of trend data 416 that is generated by the computer system 210 based on the historical data 412 and/or the study/cohort data 414. The computer system 210 may refer to the fifth table 410 in order to generate the predictions and/or recommendations 418.

The computer system 210 may generate the predictions and/or recommendations 418 for a particular study by applying the trend data 416 to the measured study/cohort data 414. For example, based on the likelihood of Group 2 participants enrolling in a study being higher than Group 3 participants, the computer system 210 may recommend that less Group 2 participants be invited to enroll in the study if the same number of participants from Groups 2 and 3 are being sought.

FIGS. 5A-5B are diagrams that illustrate example diversity assessment interface and action selection interfaces 502 a-502 c. These interfaces 502 a-502 c may be presented on the client device 204 of the researcher 202. The client device 204 may present the interfaces 502 a-502 c at one or more stages of the study based on information received from the computer system 210. As will be described in more detail below, the interfaces 502 a-502 c may present diversity measures calculated by the computer system 210, the results of a diversity analyses performed by the computer system 210, and/or recommendations generated by the computer system 210. The researcher 202 can interact with the interfaces 502 a-502 c to make various selections, such as the selections of recommendations, to make modifications (e.g., modifications to a recommendation), or to initiate one or more actions to take (e.g., one or more actions that were not recommended by the computer system 210 that the researcher 202 indicates that the computer system 210 should perform).

As will be discussed in more detail below, the recommendations may be ranked and presented in an order corresponding to their rank. The researcher 202 may interact with the interface 502 a to select actions to finalize a monitoring program, adjust a monitoring program, select recommended actions to adjust a monitoring group, or make other adjustments.

FIG. 5A illustrates example diversity assessment and action selection interface 502 a during a cohort selection stage of a study. The interface 502 a may be presented on the client device 204. As an example, the interface 502 a may be presented on the client device 204 after (e.g., in response to) the researcher 202 submitting a research question or study objective (e.g., optionally along with other study information initially submitted by the researcher 202). The interface 502 a may present various diversity metrics calculated by the computer system 210 and recommendations generated by the computer system 210. The researcher 202 may interact with the interface 502 a to, for example, select a recommendation for the computer system 210 to perform, adjust study parameters, select users to enroll in a study, select users to be invited to the study, etc.

The interface 502 a includes a study criteria section 510 a, a diversity analysis results section 520 a, and a recommendation section 530 a. The researcher 202 may, for example, use the interface 502 a to review recommendations generated by the computer system 210 at a beginning stage of the study. The researcher 202 may, through the interface 502 a, select one or more recommended actions to be performed.

The researcher 202 may be able to also use the interface 502 a to indicate one or more actions to be performed, e.g., that may not have been recommended. For example, the researcher 202 may, through the interface 502 a, add or remove an exclusion criterion to or from the exclusion criteria 516.

As shown, the study criteria section 510 a may include various parameters for a given study. For example, the study criteria section 510 a may include a cohort size 511, a study length 512, a study region 513, study requirements 514, and a target data 517.

In addition to study parameters, the study criteria 510 a may also include inclusion criteria 515 a that enrolled participants are required, at a minimum, to meet, and exclusion criteria 516. If a candidate happens to meet any of the exclusion criteria 516, that candidate is automatically excluded from consideration, e.g., even if they meet the inclusion criteria 515 a and any other requirements in the study criteria 510 a. Similarly, if new information is obtained that indicates that an enrolled participant meets an exclusion criterion, the computer system 210 may automatically remove the participant from enrollment.

However, in some cases, the computer system 210 generates a warning indicating the problem, and transmit the warning to the client device 204. The computer system 210 may refrain from removing the participant until it receives a confirmation or instructions to do so from the client device 204. The computer system 210 may, however, change how it interacts with the participant. For example, if the study involves taking a new drug, the computer system 210 may send a notice to the participant to immediately stop taking the drug, to contact their doctor, to go to the hospital, etc. Depending on the possible severity associated with the exclusion criteria (e.g., increased likelihood of a serious allergic reaction by 500%), the computer system 210 may contact a doctor for the participant and/or emergency services for the participant (e.g., if the participant does not respond to an initial message with a threshold amount of time).

The study criteria may be set by the researcher 202, the computer system 210, or a combination of the researcher 202 and the computer system 210.

The diversity analysis results section 520 a depicts the results of a diversity analysis performed by the computer system 210 at the start of a study. The diversity analysis presented may be based on a research question provided by the researcher 202 and/or the study criteria 510 a. This diversity analysis may reflect a diversity analysis performed before any participants have been enrolled in the study, and/or before any candidates have been invited to participate in the study.

As shown, the diversity analysis may include a determined reference population 521, a target group composition 522 (e.g., for the enrolled participant at the completion of the study), and identified previous participants 535. The identified previous study participants 523 may be all participants that are relevant to the study. For example, if the study calls for monitoring participants from Group 1, Group 2, and Group 3, the previous study participants 523 may include all previous participants of those groups. As another example, the previous study participants 523 may include only those participants that meet the study criteria 510 a, e.g., meet the requirements 514, meet the inclusion criteria 515 a, don't meet any of the exclusion criteria 516, live in or sufficiently near the study region 513, etc.

In some cases, the previous study participants 523 may also include participants who have registered for a study (e.g., registered for this current study) but have not necessarily participated in or completed a previous study.

The recommendation section 530 a may include one or more recommendations generated by the computer system 210 using the diversity analysis results. The computer system 210 may generate one or more recommendations using the diversity analysis results in order to achieve the target group composition 522.

As shown, a first recommendation option 532 is displayed in the interface 502 a and has been selected (e.g., by the researcher 202). This recommendation option 532 includes a modification to the study criteria 510 a. Specifically, the recommendation option 532 provides for removing one of the inclusion criterion from the inclusion criteria 515 a. The recommendation option 532 also provides that invitations to enroll will be sent to all previous study participants 523, which includes, for example, 915 participants from Group 1, 211 participants from Group 2, and 201 participants from Group 3. The previous study participants 523 may be limited to those participants that have successfully completed a study, that completed a study with an average activity level that meets a threshold activity level, that maintained an activity level above a threshold activity level, that participated in a threshold number of previous studies, that completed a threshold number of previous studies, etc.

The recommendation section 530 a also includes a second recommendation option 534. The computer system 210 may provide for presenting the recommendation option 532 above the recommendation option 534 based on an anticipated diversity level associated with the recommendation option 532 being greater than an anticipated diversity level associated with the recommendation option 534. That is, the computer system 210 may rank the different recommendation options and display the different recommendations options in accordance with their respective ranks.

The diversity level may indicate how close the predicated group composition at study completion is to the target group composition. The diversity level may be, for example, a diversity score. That is, the diversity level may be a single value that is indicative of how close the predicted group composition at study completion is to the target group composition. As an example, a diversity score of 1.0 may indicate that the predicted group composition at study completion matches the target group composition 522. The diversity score may be absolute, or it may be relative, e.g., relative to a previously predicted group composition at study completion or relative to the predicted group composition at study completion of one or more other recommendation options. Additionally or alternatively, the diversity level may be, for example, a calculated distribution (e.g., probability distribution). This diversity distribution may, for example, indicate probabilities of achieving the target group composition 522 (e.g., after performing actions corresponding to a particular recommendation option) and/or probabilities associated with different possible group compositions at study completion.

The diversity level may indicates a level of confidence in achieving the target group composition 522, and/or achieving a group composition that is with an acceptable range (e.g., percent range or value range) of the target group composition 522. For example, a diversity score of 0.91 may indicate that the computer system 210 has determined that there is 91% possibility of the group composition at study completion being within a threshold percent (e.g., 5%, 3%, 1%, etc.) of the target group composition 522 provided that the actions corresponding to the recommendation option 532 are performed.

Diversity level may also or alternatively describe a group composition, or the difference between a group composition (e.g., current or predicted) and the target group composition 522. For example, a predicted group composition at study enrollment may be a first diversity level, a predicted group composition at study completion may be a second diversity level, and a difference (e.g., difference between two sets of values, absolute value of the difference between the two sets of values, etc.) the group composition at study completion and the target group composition 522 as a third diversity level.

In some cases, there are multiple diversity levels (e.g., diversity metrics) that include both one or more singular values, and one or more distributions. For example, a first diversity level may include a diversity distribution indicating different likelihoods of achieving the target group composition 522, and a diversity score may be second diversity level identified from the diversity distribution (e.g., as the value associated with the highest probability out of the all of the values).

As discussed above, the computer system 210 may rank the recommendations based on one or more diversity metrics (e.g., diversity levels) calculated for the recommendations. For example, the computer system 210 may rank the recommendations presented in the recommendation section 530 a according to a calculated anticipated diversity score for each of the recommendations (e.g., that indicates the anticipated diversity of the cohort at the end of the study should the actions in the corresponding recommendation be performed). The anticipated diversity score is likely to be higher if the actions in a recommendation are predicted to produce a group composition that matches or gets sufficiently close (e.g., with respect to the performance of actions in other recommendations) to the target group composition 522. The computer system 210 may provide instructions to the client device 204 to have the recommendations presented on the interface 502 c according to their rank. For example, the computer system 210 may provide instructions to the client device 204 to present the recommendation option 532 above the recommendation option 534 based on the anticipated diversity score (e.g., at the end of the study) associated with the recommendation option 532 being greater than the anticipated diversity score associated with recommendation option 534. By ranking the recommendations according to their influence in achieving the target group composition 522 and, therefore, their influence in on the study's ability to produce viable data, the computer system 210 can (i) more efficiently present its recommendations to the researcher, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204).

In some implementations, computer system 210 may only recommend a threshold number of recommendation options (e.g., for display on the client device 204) and/or only send a threshold number of recommendation options to the client device 204. For example, the computer system 210 may only recommend the two, three, or four highest ranking recommendation options for display on the client device 204. The threshold may be selected by the researcher 202 or may be automatically determined by the computer system 210. As an example, the computer system 210 may determine the threshold based on diversity scores associated with the different recommendations, and/or based on the difficulty of the actions in the recommendation options. In more detail, the computer system 210 may generate instructions to present less recommendation options if there are significant diversity score differences (e.g., greater than 5%, 10%, or 20% divergence with respect to the top three, four, or five highest ranking recommendation options) between the different recommendation options such that there are recommendation options that are clearly superior to other recommendation option, or may present more recommendation options if there are insignificant diversity score differences between the different recommendation options (e.g., less than 2%, 5%, or 10% divergence with respect to the top three, four, or five highest ranking recommendation options) such that there are multiple recommendation options that are likely to produce similar diversity results. By presenting only a subset of recommendations, the computer system 210 can (i) more efficiently present the key recommendations to the researcher, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204) by removing the need to devote screen space to recommendations that are unlikely or less likely to achieve the diversity needs of the study.

Prior to recommending a set of actions, the computer system 210 may first ensure that the corresponding prospective recommendation meets certain criteria. For example, the computer system 210 may first apply a minimum anticipated diversity level threshold to the recommendation before it can be presented on a display of the client device 204 and/or sent to the client device 204. For example, the computer system 210 may apply a static threshold of 0.90 to the anticipated diversity level. This would have the effect of permitting only the first recommendation option 532 from being displayed in the interface 502 a. The diversity level threshold may instead be dynamic, e.g., based on a current anticipated diversity level, based on historical data for the groups being invited to participate in the study, based on the trends for the groups being invited to participate in the study, etc. For example, the computer system 210 may only allow recommendations that result in the anticipated diversity level improving by at least 5% with respect to a current anticipated diversity level at completion. By presenting only a subset of recommendations that meet certain quality criteria, the computer system 210 can (i) more efficiently present the key recommendations to the researcher that are likely to have a minimum beneficial effect on meeting the diversity needs of the study, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204) by removing the need to devote screen space to recommendations that are unlikely or less likely to achieve the diversity needs of the study.

The recommendations determined by the computer system 210 may also include recommendations to mitigate risks presented by the inclusion of high risk groups in the study. The inclusion of these high risks groups can benefit the diversity of the study so that the study can achieve its diversity needs (e.g., the target group composition 522, a minimum number of participants from Group 1, a minimum number of participants from Group 2, a minimum number of participants from Group 3, a minimum activity level for each of the participants or minimum average activity level for each group of participants, etc.). However, the inclusion of these high risk groups can also present additional risks that may need to be mitigated in other ways, such as through the addition, removal, or modification of study parameters (or for the provision other study actions) for those participants in these at risk groups.

The computer system 210 may identify a group of persons as at risk based on the group being tagged or preprogramed as at risk in the database 212 (e.g., tagged by the researcher 202 or tagged by a previous researcher) such as elderly persons, based on historical data showing disproportionately high incidents (e.g., side effects from pharmaceuticals, hospitalizations, death, etc.) for those in the at risk group, etc. After determining that one or more participants in the at risk group are to be enrolled in the study or invited to enroll in the study, the computer system 210 may generate one or more recommendations to mitigate the risks presented to these groups, such as a recommendation to provide additional monitoring for those in the at risk group, a recommendation for more frequent data collection for those in the at risk group, a recommendation to reduce the medication dosage for those in the at risk group to spread the vaccine administration over a longer period of time for those in the at risk group, a recommendation for the medication or vaccine to be administered only by a doctor, a recommendation for the medication or vaccine to be administered only by a doctor in a hospital, etc.

FIG. 5B illustrates example diversity assessment and action selection interface 502 b during a participant enrollment stage of a study. The interface 502 b may be presented on the client device 204. As an example, the interface 502 b may be presented on the client device 204 at an enrollment stage of the study. Specifically, the interface 502 b may be presented after (e.g., in response to) all or threshold percent of users have responded to enrollment invitations, after a threshold amount of time has passed since enrollment invitations were sent out, after a threshold number of users have enrolled, etc. The interface 502 b may present various diversity metrics calculated by the computer system 210 and recommendations generated by the computer system 210 for this stage of the study. The researcher 202 may interact with the interface 502 b to, for example, select a recommendation for the computer system 210 to perform, adjust study parameters, select new users to enroll in a study, select new users to be invited to the study, select users to remove from enrollment, select users to have their invitations revoked, etc.

The interface 502 b includes an updated study criteria section 510 b, a diversity analysis results section 520 b, and a recommendation section 530 b. The researcher 202 may, for example, use the interface 502 b to review recommendations generated by the computer system 210 at an enrollment stage of the study. The researcher 202 may, through the interface 502 b, select one or more recommended actions to be performed.

The researcher 202 may be able to also use the interface 502 b to indicate one or more actions to be performed, e.g., that may not have been recommended. For example, the researcher 202 may, through the interface 502 b, add or remove an inclusion criterion to or from the inclusion criteria 515 b.

As shown, the study criteria section 510 b may include various parameters for a given study. For example, the study criteria section 510 b may include a cohort size 511, a study length 512, a study region 513, study requirements 514, and a target data 517.

In addition to study parameters, the study criteria 510 b also include updated inclusion criteria 515 b and exclusion criteria 516. The inclusion criteria 515 b has been updated based on a selected recommendation so that an inclusion criterion has been removed. This has the effect of increasing the candidate pool of potential participants.

The diversity analysis results section 520 b depicts the results of a diversity analysis performed by the computer system 210 at an enrollment stage the study. The diversity analysis presented may be based on a research question provided by the researcher 202, the study criteria 510 b, the candidates invited to enroll and/or the candidates that have applied to enroll, and/or the actual enrollment of the study.

As shown, the diversity analysis results section 520 b may include the target group composition 522 (e.g., for the enrolled participant at the completion of the study), an enrolled group composition 524 (e.g., indicating the diversity of participants who have actually enrolled in the study), and a predicted group diversity at completion 525. The computer system 210 may determine the predicted group diversity at completion 525 based on, for example, a combination of historical data or trends determined from historical data, and the enrolled group composition 524.

The computer system 210 may compare the predicted group diversity at completion 525 to the target group composition 522. If the predicted group composition 525 falls outside of a target composition range (e.g., based on the target group composition 522), then the computer system 210 may generate a warning 527 indicating that it is anticipated that the target group composition 522 will not be achieved.

As another example, the warning 527 may be generated by the computer system 210 in response to determining that the diversity level 526 does not meet a threshold diversity level. For example, the computer system 210 may compare the diversity level 526 to a threshold diversity level of 0.90. Based on the diversity level 526 being below the threshold diversity level, the computer system 210 may generate the warning 527 or a similar warning (e.g., indicating low diversity level and/or that the target group composition is unlikely to be achieved by study completion), and transmit the warning to the client device 204.

In some cases, the computer system 210 may compare the diversity level 526 to multiple thresholds. These thresholds may correspond to different actions performed by the computer system 210. For example, if the diversity level does not meet a first threshold but does meet a second threshold, the computer system 210 may generate a low priority warning and transmit it to the client device 204. However, if the diversity level does not meet the first threshold and the second threshold, the computer system 210 may generate a high priority warning, transmit the warning to the client device 204, and automatically perform one or more actions to account for the low diversity level. For example, the computer system 210 may determine one or more actions to take to improve the diversity level, such as invite persons to enroll from an underrepresented group, remove persons from an overrepresented group, remove or modify inclusion or exclusion criteria, adjust the study parameters, etc.

The recommendation section 530 b may include one or more recommendations generated by the computer system 210 using the diversity analysis results indicated in the diversity analysis results section 520 b. The computer system 210 may generate one or more recommendations using the diversity analysis results in order to achieve the target group composition 522. Specifically, the computer system 210 may determine a first recommendation option 536 based on or in response to the warning 527. The first recommendation option 536 may be a recommendation to perform one or more actions so that the predicated group diversity at completion 525 will match the target group composition 522 or will be within a target diversity range (e.g., that is based on the target group composition 522).

FIG. 5C illustrates example diversity assessment and action selection interface 502 c during a participant enrollment stage of a study. The interface 502 c may be presented on the client device 204. As an example, the interface 502 c may be presented on the client device 204 at an enrollment stage of the study. Specifically, the interface 502 c may be presented after (e.g., in response to) all or threshold percent of users have responded to enrollment invitations, after a threshold amount of time has passed since enrollment invitations were sent out, after a threshold number of users have enrolled, etc. The interface 502 c may present various diversity metrics calculated by the computer system 210 and recommendations to adjust enrollment generated by the computer system 210 for this stage of the study. The interface 502 c may include various interface elements that allow a user to quickly consider and act on recommendations to invite or enroll new users in the study, remove users from enrollment, or replace users who are currently enrolled. The researcher 202 may interact with the interface 502 c to, for example, select new users recommended by the computer system 210 to enroll in a study, select new users recommended by the computer system 210 to be invited to the study, select users recommended by the computer system 210 for removal from enrollment, select users recommended by the computer system 210 for replacement, select users recommended by the computer system 210 for having their invitations revoked, etc.

As another example, the interface 502 c may be presented on the client device 204 at one or more later stages of the study (e.g., after some of study data has been obtained from the participant devices). Specifically, the interface 502 c may be presented after (e.g., in response to) a participant leaving the study, a participant's activity level dropping below a threshold activity level, a diversity score falling below a diversity score threshold, etc.

The interface 502 c includes the updated study criteria section 510 b, the diversity analysis results section 520 b, and an enrollment section 540. The researcher 202 may, for example, use the interface 502 c to adjust the enrollment for the study at the start of the study or at one or more later points in time during the study.

As shown, the study criteria section 510 b may include various parameters for a given study. For example, the study criteria section 510 b may include a cohort size 511, a study length 512, a study region 513, study requirements 514, and a target data 517. In addition to study parameters, the study criteria 510 b also include updated inclusion criteria 515 b and exclusion criteria 516.

The diversity analysis results section 520 b depicts the results of a diversity analysis performed by the computer system 210 at an enrollment stage the study. The diversity analysis presented may be based on a research question provided by the researcher 202, the study criteria 510 b, the candidates invited to enroll and/or the candidates that have applied to enroll, and/or the actual enrollment of the study.

As shown, the diversity analysis may include the target group composition 522 (e.g., for the enrolled participant at the completion of the study), the enrolled group composition 524 (e.g., indicating the diversity of participants who have actually enrolled in the study), and the predicted group diversity at completion 525. The computer system 210 may determine the predicted group diversity at completion 525 based on, for example, a combination of historical data or trends determined from historical data, and the enrolled group composition 524.

As discussed above, using the target group composition 522 and at least one of the enrolled group composition 524 and the predicted group composition 525, the computer system 210 calculates a diversity level 526. As shown, the diversity level 526 is an anticipated diversity level at study completion, e.g., that is indicative of the difference (e.g., actual or percent difference) between the predicted group composition 525 and the target group composition 522.

The computer system 210 may compare the predicted group diversity at completion 525 to the target group composition 522. If the predicted group composition 525 falls outside of a target composition range (e.g., based on the target group composition 522), then the computer system 210 may generate the warning 527 indicating that it is anticipated that the target group composition 522 will not be achieved.

As another example, the warning 527 may be generated by the computer system 210 in response to determining that the diversity level 526 does not meet a threshold diversity level. For example, the computer system 210 may compare the diversity level 526 to a threshold diversity level of 0.90. Based on the diversity level 526 being below the threshold diversity level, the computer system 210 may generate the warning 527 or a similar warning (e.g., indicating low diversity level and/or that the target group composition is unlikely to be achieved by study completion), and transmit the warning to the client device 204.

In some cases, the computer system 210 may compare the diversity level 526 to multiple thresholds. These thresholds may correspond to different actions performed by the computer system 210. For example, if the diversity level does not meet a first threshold but does meet a second threshold, the computer system 210 may generate a low priority warning and transmit it to the client device 204. However, if the diversity level does not meet the first threshold and the second threshold, the computer system 210 may generate a high priority warning, transmit the warning to the client device 204, and automatically perform one or more actions to account for the low diversity level. For example, the computer system 210 may determine one or more actions to take to improve the diversity level, such as invite persons to enroll from an underrepresented group, remove persons from an overrepresented group, remove or modify inclusion or exclusion criteria, adjust the study parameters, etc.

The enrollment section 540 may present more detail information on the currently enrolled participants, and/or may present options, such as recommended options, for adjusting the enrollment of the study. As shown, the enrollment section 540 may include a table 542 that includes a first column the displays that displays users that the computer system 210 recommends to be invited for enrollment and/or added to enrollment, and a second column that displays users that the computer system 210 has marked for possible removal or replacement.

Factors for recommending users for invitation, addition, removal, or replacement may include the group(s) associated with the user and an expected participation for the user. The computer system 210 may recommend the addition of users that are associated with an unrepresented group or a group that is anticipated to be unrepresented at study completion. Similarly, the computer system 210 may recommend the remove or replacement of users that are associated with an overrepresented group or a group that is anticipated to be overrepresented at study completion. As an example, the computer system 210 may be more likely to recommend Group 2 users based on the predicted group composition 525 indicating that the anticipated enrollment of Group 2 participants will be significantly lower than that indicated in the target group composition 522. The computer system 210 may also recommend users that have a higher expected participation as they will be more likely to provide data and/or consistently provide data required for a study, and/or more likely to complete the study.

The table 542 may also indicated the predicted effect that the addition, removal, or replacement of the specific users may have on the study. As an example, the computer system 210 may determine the anticipated diversity level at study completion should the corresponding user be added, removed or replaced. For example, the addition of the User S to the study is anticipated to raise the diversity level from 0.87 to 0.91.

The researcher 202 may interact with the table 542 to select one or more users to add/invite. Similarly, the researcher 202 may interact with the table 542 to select one or more users to remove or replace. If the researcher 202 select to replace a user, they the client device 204 may prompt the researcher 202 to select a replacement user to invite/add.

The recommendation for the addition, invitation, removal, or replacement of participants may further be based on the past studies that the participants have previously been a part of. For example, candidates may be more likely to be recommended for invitation and/or addition (e.g., addition may be an option if the participant has already agreed to enrolled or applied to enroll) if the participants have enrolled in a previous research study, have completed a previous research study, have enrolled in a relatively high number of research studies (e.g., compared to other candidates, enrolled participants, etc.), have completed a relatively high number of research studies, and/or have completed a threshold percent of research studies that they previously enrolled in. In contrast, enrolled participants may be more likely to be marked for removal or replacement if, for example, they have not enrolled or completed a previous research study (e.g., which would introduce a great deal of uncertainty such that there may be, for example, insufficient information to anticipate whether the participant will complete the study and/or the expected participation of the participant), have enrolled in or completed a relatively low number of studies, and/or have a low study completion percentage.

The expected participation of the candidates and the enrolled participants may be determined using information obtained from past studies. For example, the computer system 210 may access a previously calculated expected participation for an enrolled participant from a user profile for the participant. As another example, the computer system 210 may access study data stored and associated with a user, and calculated, based on the study data, an expected participation level of the user. The expected participation level may be based on, for example, a user's task completion percentage, task completion time, inquiry response time, data upload consistency, etc.

The computer system 210 may rank the users that it recommends for invitation, enrollment, removal, or replacement. For example, the users may be ranked based on the anticipated diversity level calculated by the computer system 210 should the recommendation be acted on. The effects of a user being added or invited to a study on the anticipated diversity level are likely to increase more significantly if the user belongs to a currently underrepresented group or anticipated unrepresented group (e.g., anticipated to be unrepresented by the end of the study, such as the Group 2 participants that are currently overrepresented but are anticipated to be underrepresented) than if the user belongs to a currently overrepresented or anticipated overrepresented group (e.g., Group 1 participants that are currently underrepresented but anticipated to be overrepresented by the end of the study). Additionally or alternatively, the users may be ranked based on their expected activity level in the study (e.g., based on historical data indicating their past activity levels, past study completion rates, etc.). This however may be taken into account by the anticipated diversity level since the anticipated diversity level may correspond to the predicted group composition 522 at the end of the study and a lower expected activity level associated with a user would indicate a higher likelihood of the user not completing the study and/or not producing sufficient data needed for the study.

The computer system 210 may provide instructions to the client device 204 to have the recommended users presented in the interface 502 c according to their rank. For example, as shown, User S may be presented above User R based on the anticipated diversity level of 0.94 (e.g., by the end of the study) for inviting or enrolling User S being greater than the anticipated diversity level of 0.93 for inviting or enrolling User R. By ranking the user recommendations according to their influence in achieving the target group composition 522 and, therefore, their influence in on the study's ability to produce viable data, the computer system 210 can (i) more efficiently present its recommendations to the researcher, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204).

In some cases, the client device 204 may present the user recommendations as a list that is not necessarily organized by the recommended action. The list may instead be ordered based on the anticipated diversity level and/or the expected participation level. For example, the client device 204 may present a list of user recommendations starting with inviting User S (e.g., based on being associated with the highest diversity level), followed by inviting User R (e.g., based on being associated with the second highest diversity level), followed by removing or replacing User D (e.g., based on being associated with the third highest diversity level), and followed by removing or replacing User B (e.g., based on being associated with the fourth highest diversity level).

Similarly, the computer system 210 may only recommend a subset of the available users based on their determined ranks. For example, the computer system 210 may only recommend a threshold number of user recommendations (e.g., a total of only three, four, or six user recommendations are initially presented to the researcher 202 on the client device 204 unless the researcher 202 requests additional recommendations) or a threshold number of user recommendations for each specific action (e.g., a maximum of three invitation or enrollment recommendations are presented on the client device 204, and a maximum of two removal or replacement recommendation are presented on the client device 204) selected accordingly to their respective ranks. The thresholds may be selected by the researcher 202 or may be automatically determined by the computer system 210. As an example, the computer system 210 may determine the thresholds based on the total number of participants currently enrolled in the study and/or on a target number of participants for the study. By presenting only a subset of user recommendations, the computer system 210 can (i) more efficiently present the key recommendations to the researcher, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204) by removing the need to devote screen space to recommendations that are unlikely or less likely to achieve the diversity needs of the study.

The computer system 210 may apply additional criteria to the user recommendations. For example, the computer system 210 may apply one or more thresholds that indicate the maximum number of recommendations presented on the interface 502 c. However, the computer system 210 may first apply a minimum anticipated diversity level threshold to the recommendation before it can be presented on a display of the client device 204 and/or sent to the client device 204. For example, the computer system 210 may apply a static threshold of 0.90 to the anticipated diversity level. This would have the effect of only the recommendations of inviting User S, inviting User R, and removing or replacing User D being displayed on the interface 502 c. The diversity level threshold may instead be dynamic, e.g., based on the current anticipated diversity level at completion 526, based on historical data for the groups and/or participants enrolled in study, based on the trends for the groups and/or participants enrolled in the study, etc. For example, the computer system 210 may only allow user recommendations that result in the anticipated diversity level improving by at least 5% with respect to the current anticipated diversity level at completion 526. By presenting only a subset of user recommendations that meet certain quality criteria, the computer system 210 can (i) more efficiently present the key recommendations to the researcher that are likely to have a minimum beneficial effect on meeting the diversity needs of the study, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204) by removing the need to devote screen space to recommendations that are unlikely or less likely to achieve the diversity needs of the study.

The computer system 210 may apply similar thresholds to the expected participation level. For example, the computer system 210 may apply a first static or dynamic threshold (e.g., relative to the activity level of other candidates, of currently enrolled users, activity level of other candidates in the same group as the recommended user, of currently enrolled users in the same group as the recommended user, etc.) that indicates a minimum expected participation level to be recommended by the computer system 210 for an invitation to enroll in the study or to be enrolled in the study. Similarly, the computer system 210 may apply a second static or dynamic threshold (e.g., relative to the activity level of other candidates, of currently enrolled users, activity level of other candidates in the same group as the recommended user, of currently enrolled users in the same group as the recommended user, etc.) that indicates a maximum expected participation level to be recommended by the computer system 210 for removal or replacement. Again, by presenting only a subset of user recommendations that meet certain quality criteria, the computer system 210 can (i) more efficiently present the key recommendations to the researcher that are likely to have a minimum beneficial effect on meeting the diversity needs of the study, and/or (ii) take greater advantage of the limited screen space available on the client device 204 (or a display connected to the client device 204) by removing the need to devote screen space to recommendations that are unlikely or less likely to achieve the diversity needs of the study.

In some cases, a similar interface to the interface 502 c is presented at one or more points throughout the study. For example, a similar interface may be presented at the user's request to modify an enrollment of participants in the study. As another example, a similar interface may be prepared and/or presented in response to certain research milestones being met, in response to a diversity warning or error being generating (e.g., due to participants associated with particular groups leaving the study; participants of particular groups not being sufficiently active in the study; etc.

Although various examples described throughout this disclosure provide for a target group composition or target group diversity including one or more target percentages for one or more groups of devices or users, the target group composition or target group diversity may alternatively provide, for each of multiple categories or types of devices or users, a target number for that category or type (e.g., a quota representing a total or minimum number to include). For example, the target group composition 522 may include a first quota of at least five-hundred users from Group 1, a second quota of at least two-hundred and fifty users from Group 2, and a third quota of at least two-hundred and fifty users from Group 3. In these examples, the relative composition of the group populations may not matter or may be a secondary factor when compared to the goal of meeting the quotas.

In some cases, there is a target group composition and one or more quotas that must be met. For example, the computer system 210 may set the target group composition 522 to 50% for Group 1 participants, 25% for Group 2 participants, and 25% for Group 3 participants, and set an acceptable target composition range as 48-52% Group 1 participants; 24-26% Group 2 participants, and 24-26% Group 3 participants. However, the computer system 210 may also set quotas for each of the groups. The target group composition and the quotas may be set in order to ensure the viability of the study results. For example, the target group composition and the quotas may be set in order to ensure (or significantly increase the likelihood) that statistically relevant results can be obtained from the study.

In some cases, the interfaces 502 a-502 c present different options and/or functionality depending on the current stage of the study, detected events, and/or obtained data. For example, the interface 502 a may be presented at a study parameter stage of the study (e.g., the second stage 234 shown in FIG. 2), an inclusion and exclusion criteria stage of the study (e.g., the third stage 236), or a select cohort stage of the study (e.g., the fourth stage 238). In contrast, the interface 502 b and/or the interface 502 c may be presented at an enroll participants stage of the study (e.g., fifth stage 240). Based on the different stages associated with the interfaces, the client device 204 may present (e.g., based on instructions provided by the computer system 210) different options for the researcher 202 to interact with. For example, the client device 204 may present options in the interface 502 a for the researcher 202 to select, modify, add, and/or remove study parameters. In contrast, in the client devices 204 may not immediately present these options in the interfaces 502 b and 502 c (e.g., although the researcher 202 may be able to still access them). Instead, the client device 204 may present in the interfaces 502 b and 502 c different options to view details of those currently enrolled in the study, to invite or enroll new users, to remove or replace enrolled users, to view details of candidates such as historical data of previous study participants, to view in real-time or substantially real-time the effects of different user selection scenarios (e.g., to see the effects on a calculated diversity score, on the anticipated group composition 522 at the end of the study, on anticipated participation level by group by the end of the study, on an anticipated participation level by group over the course of the study, on an anticipated group composition over the course of the study, etc.), etc.

FIGS. 6A-6B are diagrams that illustrate group predictions for a research study. These predications may be generated by the computer system 210 during the performance of diversity analyses. In generating the predictions, the computer system 210 may access the table data shown and described above with respect to FIG. 4. Specifically, the computer system 210 may use previously determined trend data to make predications regarding specific groups of participants. The computer system 210, may also make predications for specific participants. These predictions can be used to update or modify the predications or the prediction calculations for the groups that the specific participants belong to.

FIG. 6A is a diagram that illustrates group predictions for a research study, such as a clinical trial, or other monitoring program. The predictions may be made by the computer system 210 described above with respect to FIGS. 2-4. The computer system 210 may use the predications to determine the predicted group composition at a future time, such as at study completion.

A first graph 602 a indicates measured and predicted group participation levels. As illustrated by a key 630 a, the graph 602 a depicts measured or observed study participation levels, previous predictions made or trends corresponding to a time before the current time (t3), and a current prediction or trend corresponding to the current time (t3). For example, the computer system 210 may have, based on one or more data points corresponding to a start of study time (t1) and known trends for Group 1 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a prediction 610 a corresponding to the start of study time (t1). The prediction 610 a indicates, for example, that anticipated study participation levels of Group 1 participants at one or more future points in time with respect to the start of study time (t1). For example, the prediction 610 a indicates that the Group 1 participants are most likely to have a study participation level of 66% by the end of the study (t8). The prediction 610 a may be a trend line for Group 1 that is applied to one or more observed data points, such as a measured participation level of 87% at the start of the study.

Similarly, the computer system 210 may have, based on one or more data points corresponding to a start of study time (t1) and known trends for Group 2 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a prediction 612 a corresponding to the start of study time (t1). The prediction 612 a indicates, for example, that anticipated study participation levels of Group 2 participants at one or more future points in time with respect to the start of study time (t1). For example, the prediction 612 a indicates that the Group 2 participants are most likely to have a study participation level of 49% by the end of the study (t8). The prediction 612 a may be a trend line for Group 1 that is applied to one or more observed data points, such as a measured participation level of 74% at the start of the study.

The computer system 210 may have, based data measured (e.g., received, observed, and/or collected) over the time range of t1-t2 and known trends for Group 1 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a second prediction 610 b corresponding to a second time (t2). The prediction 610 b indicates, for example, that anticipated study participation levels of Group 1 participants has significantly changed such that the Group 1 participants are now anticipated to have a study participation level of 78% by the end of the study (t8). The computer system 210 also determines an updated prediction 612 b for the Group 2 participants indicating a new anticipated participation level of Group 2 participants of 55% by the end of the study.

Finally, the computer system 210 may have, based on data measured from the start of the study (t1) to the current time (t3) generated a current study prediction for the Group 1 participants and the Group 2 participants. The new prediction for the Group 1 participants indicates modest changes to the anticipated study participation levels that does not raise any alarms. In contrast, a significant dip in the measured study participation levels for the Group 2 participants (and/or other indicators) has resulted in the predicted study participation levels of Group 2 participants to drop significantly. The computer system 210 may generate a warning in response to the current prediction for the Group 2 participants. For example, the computer system 210 may generate a warning based on a slope of a current prediction trend line, based on anticipated participation levels of the Group 2 participants dropping below one or more threshold study participation levels that correspond to one or more different times, etc.

As an example, participants may be automatically unenrolled from a study if there participation drops below a certain threshold (e.g., 40%). As such, because the most recent prediction for the Group 2 participants indicates an average study participation level below 40% at one or more points (e.g., before or at the end of the study), the computer system 210 may, in response, generate a warning to transmit to the client device 204. The warning may be accompanied with one or more recommendations on how to address the low study participation of Group 2 participants. Alternatively, the computer system 210 may automatically perform one or more actions in attempt to address the identified issues. As will be discussed in more detail with respect to FIG. 6B, the computer system 210 may remove a study parameter from the study for Group 2 participants that, for example, the historical data 412 has shown to have a negative effect on the participation levels (e.g., and therefore the enrollment) of Group 2 participants. This cause and effect may be depicted, for example, in table 404 of FIG. 4. The study actions recommended or performed by the computer system 210 may be depicted for example, in table 410 of FIG. 4.

In general, trend lines determined and/or applied to make predictions for group participants based on various factors. For example, a trend line applied for a particular group may be determined for and/or otherwise specific to a particular time or stage of a study (e.g., particular percentage of the study that is complete), such that there may be multiple different or possible trend lines for a given group of participants. Similarly, different group trend lines may additionally or alternatively correspond to different ranges of observed or measured values. For example, the computer system 210 may apply a first trend line for study participation of Group 1 participants if a measured data point for the start of study time falls between 80% and 90% participation, and a second trend line if the measured data point for the start of study time falls between 70% and 80% participation. Accordingly, here, the computer system 210 may apply the first trend line to the Group 1 start of study measured data point(s) to generate the prediction 610 a.

The computer system 210 may make new predictions at different points throughout the study. For example, the computer system 210 may make a new prediction after a threshold amount of time has passed, after a particular study completion percentage is reached or added since a last prediction was made (e.g., prediction is made every time is determined that the current study completion percentage is 5% closer to 100% from a prior study completion percentage corresponding to when an immediately preceding prediction was made), in response to new data being collected or received (e.g., from the study participants), after a milestone in the study is reached, and/or after a stage or phase of the study changes. For example, the graphs 602 a and 604 a may be generated in response to receiving measuring new data from the study participants, and/or weekly based on data received and/or collected from study participants over the last week.

A second graph 604 a indicates measured and predicted group diversity levels (e.g., diversity of enrolled participants). As shown, the most recent group diversity predictions indicate very low retention of Group 2 participants such that it is very unlikely that the target cohort diversity will be achieved. This low retention of Group 2 may be based, at least in part, on the low study participation levels observed and/or predicted. As an example, the computer system 210 may generate the group diversity prediction based, at least in part, on the measured and/or predicted study participation of the different groups.

As an example, the computer system 210 may have, based on one or more data points corresponding to a start of study time (t1) and known trends for Group 1 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a prediction 614 a corresponding to the start of study time (t1). The prediction 614 a indicates, for example, an anticipated Group 1 enrollment relative to one or more other groups in the study. For example, the prediction 614 a indicates that the Group 1 participants are most likely to make up about 50% of the study enrollment by the end of the study (t8), e.g., which is in line with a Group 1 enrollment goal to achieve the cohort composition target 224 by the end of the study. The prediction 614 a may be a trend line for Group 1 that is applied to one or more observed data points, such as a measured enrollment percentage of Group 1 participants of 42% at the start of the study.

Similarly, the computer system 210 may have, based on one or more data points corresponding to a start of study time (t1) and known trends for Group 2 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a prediction 616 a corresponding to the start of study time (t1). The prediction 616 a indicates, for example, an anticipated Group 2 enrollment relative to one or more other groups in the study. For example, the prediction 616 a indicates that the Group 2 participants are most likely to make up about 25% of the group composition 225 by the end of the study (t8), e.g., which is in line with a Group 2 enrollment goal to achieve the cohort composition target 224 by the end of the study. The prediction 616 a may be a trend line for Group 2 that is applied to one or more observed data points, such as a measured enrollment percentage of Group 2 participants of 33% at the start of the study.

The computer system 210 may have, based data measured (e.g., received, observed, and/or collected) over the time range of t1-t2 and known trends for Group 1 participants (e.g., as indicated in the trend data 416 shown in FIG. 4, such as the table 402), generated a second prediction 614 b corresponding to a second time (t2). The prediction 614 b indicates, for example, that anticipated study participation levels of Group 1 participants has modestly changed such that the Group 1 participants are now anticipated to make up roughly 53% of the group composition 226 by the end of the study. The computer system 210 also determines an updated prediction 616 b for the Group 2 participants that the Group 2 participants are now anticipated to make up 23% of the group composition 226 by the end of the study. These changes may, in some cases, be enough to trigger the computer system 210 to generate a warning, to determine one or more recommendations, and/or to automatically perform one or more actions.

Finally, the computer system 210 may have, based on data measured from the start of the study (t1) to the current time (t3) generated a current study prediction for the Group 1 participants and the Group 2 participants. The new predictions for the Group 1 and Group 2 participants indicates significant changes to the group composition 226, such that the computer system 210 may, in response, generate a warning. Notably, the current prediction indicates that the enrollment of Group 2 participants is expected to deviate significantly from the Group 2 enrollment goal of 25%, and that, relatedly, the enrollment of Group 1 participants is expected to deviate significantly from the Group 1 enrollment goal. The computer system 210 may generate a warning in response to the current prediction for the Group 1 and Group 2 participants. For example, the computer system 210 may generate a warning based on a slope of a current prediction trend line, based on an anticipated group composition 226 at the end of the study (t8), based on the enrollment percentage of the Group 2 participants dropping below a threshold at one or more current and/or future points in time, based on the enrollment percentage of the Group 1 participants exceeding a threshold at one or more current and/or future points in time, etc.

FIG. 6B is a diagram that illustrates updated group predictions for a research study. The predictions may be made by the computer system 210 described above with respect to FIGS. 2-4. The computer system 210 may use the predications to determine the predicted group composition at a future time, such as at study completion.

As illustrated in FIG. 6B and by the key 630 b, time has elapsed and the predictions have been updated accordingly when compared to FIG. 6A. The updated prediction may have been made by the computer system 210. During the elapsed time, (e.g., at the time t3) a study action of removing “Study Parameter 1” for the Group 2 participants has been performed. This study action may have been automatically performed by the computer system 210, or may have been part of a recommendation generated by the computer system 210 that was accepted by a researcher of the study. As shown, the performance of the study action has had a significant effect on the observed and predicted data (e.g., diversity related data) for the study with respect to Group 2 participants.

The first graph 602 b indicates updated measured and predicted participation levels of study participants by group last updated at a time t5. As shown, the performance of the study action to remove a study parameter for Group 2 participants has had a significant effect (here an improvement) on the participation levels of Group 2 participants.

A second graph 604 a indicates updated measured and predicted group diversity levels (e.g., diversity of enrolled participants). The measured and predicated group diversity levels were in this example last updated at a time t5. As shown, the most recent group diversity predictions indicate a significantly improved group diversity predictions such that the expected group diversity at the end of the study is now expected to be within 3% of the target cohort diversity.

FIGS. 8A-8B are diagrams that illustrate one or more components of the system 200 and a process for customizing monitoring programs involving remote devices. In customizing monitoring program, the computer system 210 of the system 200 can assess and adjust the composition of groups for a monitoring program 812 a or the monitoring program 812 a using a set of profiles 804. The computer system 210 may distribute adjusted monitoring program 812 to multiple remote devices.

The computer system 210 may be configured to distribute software for a monitoring program to devices that are to be monitored. The monitoring program may indicate times when data is to be obtained from, requested from, or sent to the monitored devices. The monitoring program may also indicate the type of data or the specific data that is to be obtained from, requested from, or sent to the monitored devices. The data obtained or requested from the monitored devices may include sensor data collected using sensors of the monitored devices or sensor devices electronically connected to the monitored devices. The data sent to the monitored devices may include instructions to collect sensor data, or updates to the monitoring program or a portion of the monitoring program on the devices. In updating the monitoring program or a portion of the monitoring program on the devices, a configuration of the monitored devices can be adjusted to, for example, change what data is monitored or how the data is monitored.

The computer system 210 can adjust the monitoring program for particular groups of devices or users. These groups may correspond to different profiles generated by the computer system 210. The computer system 210 may assign each of the monitored devices to at least one profile based on attributes of the devices or attributes of users of the devices. For example, a first profile may indicate that a device must include a GPS unit and a heartrate monitor, and that the user of the device must live in an urban environment and must be between the ages of 25 and 30 years of age. If a first device of the group of device meets the device requirements of the profile criteria and has a user that meets the user requirements of the criteria, the computer system 210 may classify the first device as belonging to the first profile.

The computer system 210 may generate the profiles based on previously observed outcomes. For example, the computer system 210 may generate profiles based on observed outcomes of a currently running and/or previously performed monitoring programs. The observed outcomes may include the compliance of the devices or their users with the requirements of the monitoring program, and the retention of the devices or their users in the monitoring program. As another example, the computer system 210 may generate profiles based on attributes of devices or users in a candidate pool. The attributes may include, for example, sensors that the devices include, sensor devices that are compatible with the devices, models of the devices, operating systems of the devices, etc. The attributes may also include demographic or non-demographic information that describes the users. The users may include, for example, users that have previously participated in a monitoring program, that are currently participating in a monitoring program, have indicated that they want to participate in a monitoring program, or that are eligible for a monitoring program.

The computer system 210 may generate the profiles using a machine learning model or a group of machine learning models. As an example, the computer system 210 may using a clustering machine learning model to cluster different devices or users based on observed outcomes. Similarly, as another example, the computer system 210 may use a clustering model to cluster different groups of devices or users based on attributes of the devices or users. The model may use all attributes available to the model in performing the clustering. Alternatively, the model may use a subset of attributes corresponding to key attributes to perform the clustering. These key attributes may be determined using another machine learning model or a group of other machine learning models, using a static algorithm or group of static algorithms, or based on input from an administrator or researcher.

In general, a monitoring program refers to a set of elements that define how to conduct a monitoring program of multiple devices and/or persons. The elements may include parameters for the monitoring program. These parameters may, for example, define inclusion criteria for persons or devices in the monitoring program, and/or exclusion criteria for persons or devices in the monitoring program. The elements may also include a definition of the monitoring program or an indication of what type of studies the monitoring program has (i) previously been used for, and/or (ii) is applicable to. The elements may also include an indication of the particular data that is to be requested and/or received during a program, a schedule that indicates when data is to be requested and/or received, and/or a frequency of data collection or reception. The elements may further define one or more conditions for determining the end of the monitoring program. For example, an element may indicate that sessions of the monitoring program are (e.g., by default) to be run for six months. As another example, an element may in dictate that sessions of the monitoring program are to be run until a set of particular conditions are met (e.g., enough data is collected from each of the participants). Similarly, the elements may define conditions for determining one or more milestones of the monitoring program.

The elements may define or otherwise indicate other information of the monitoring program, including other communication information. For example, the elements may indicate a default communication channel, a default word choice (e.g., vocabulary) for communication, a default sentence structure (e.g., formal, semi-formal, informal, etc.).

FIG. 7 is a flowchart diagram that illustrates an example process for diversity assessment and action selection. The process 700 may be performed by the computer system 110 shown in FIG. 1. The process 700 may be performed by the computer system 210 shown in FIGS. 2-4.

In many cases, the administrator of a monitoring program, such as a researcher conducting a clinical trial, cannot determine whether the monitoring group provide sufficient diversity to achieve the needs of the monitoring program. Beyond simply whether a sufficient total number of participants are enrolled, it is difficult to determine whether the makeup of the monitoring group provides the right variety in participant backgrounds. Further, the effective level of diversity can be diminished through the lack of compliance that disproportionately affects some groups over others. The process 700 helps the system determine and inform an administrator whether a monitoring group has sufficient diversity, as well as to indicate what adjustments should be made to obtain the proper diversity representation level if it is not present.

Briefly, the system can identify a reference population for the monitoring program (e.g., population at large, or a group most likely to use a drug or product) and determine a diversity goal that reflects the characteristics of the reference population. The system can also determine diversity metrics for a monitoring group (e.g., a clinical trial cohort) and provide the diversity metrics to the administrator, indicating whether the current and expected future monitoring group characteristics will meet the target level of diversity. For example, the system can compare the diversity metrics for the cohort to the goal levels (e.g., thresholds, ranges, minimums, etc.), determine that the diversity metrics are outside a desired range, and then select actions to improve diversity in the monitoring group.

The process 700 includes determining a target composition for a group to be monitored in a monitoring program (702). In some cases, the system provides a user interface through which a researcher or other administrator can specify the target composition. In other cases, the system determines the target composition, calculating an appropriate target from data describing a reference population and other data.

As discussed above, the target composition can be based on a reference population, such as a set of devices or users in a particular geographical area. The target composition can be based on other populations or subgroups. For example, for a research study about diabetes, the target composition may be based on the set of people in the United States that have diabetes, and so the target composition represents the characteristics of that population rather than the general population. The system can receive input indicating the location or general population of interest, and then retrieve population data (e.g., census data, survey data, etc.) specifying the makeup of the population. The system then sets the target composition to have characteristics (distribution of profiles or attributes) that are the same as or are based on the characteristics in the population data.

The target composition can be defined with respect to attributes that are specified by a user as relevant to the study or as inferred by the system as relevant to the study. The dimensions used for assessing composition and diversity can be different from one monitoring program to another. Some monitoring programs may define composition in terms of a desired distribution across each of age, sex, and race. Other monitoring programs may additionally or alternatively use other attributes, such as whether a person has a certain health status or not (e.g., high blood pressure, obesity, diabetes, cancer history, etc.), a certain genetic profile, or whether the user has a certain behavior pattern. Thus, the dimensions for which diversity can be assessed and achieved can be well beyond simple demographics.

The target composition can be expressed in different ways, and even with multiple types of constraints for a single monitoring program. One is a relative measure for different groups or profiles (e.g., 10% from group 1, 20% from group 2, etc.). Another is a set of minimums or quotas for each of different groups (e.g., at least 10 from group 1, at least 25 from group 2, etc.). Another is a set of ranges, thresholds, or constraints. For example, the target may be expressed as amounts or percentages of the monitoring group for each of different attribute values (e.g., a minimum of 40% male, a minimum of 40% female, a minimum of 30 individuals that have diabetes and a minimum of 30 individuals that do not have diabetes, no more than 40% Caucasian participants, at least 15% for each of multiple different racial backgrounds, etc.)

The process 700 includes determining a group composition for a group of devices or users for the monitoring program (704). The group can be a monitoring group of enrolled devices or users that have registered or subscribed to participate in the monitoring program. As another example, the group can be a group of candidates selected to invite to participate, or a candidate pool from which candidate participants can be selected. In health research studies, the group can be an actual cohort of individuals enrolled in a research study. Similarly, at the creation of the research study, before the study has begun, the group can be a candidate pool or a selected prospective cohort (e.g., a set of prospects identified as meeting the selection criteria which may not yet have enrolled).

The system can determine the measures of composition of the group for each of the different constraints or measures of diversity used to define the target. For example, if the target is a set of quotas for different participant types or profiles (e.g., defined by age, race, sex, or other attributes), the system can determine the number of people in the assessed group that meet each type or profile. Similarly, if the target is expressed in terms of percentages for different types or profiles, the group composition can also be determined as the percentages for the different types or profiles. In general, the group composition can indicate the amounts of members in different categories and the distribution of different attribute values among the group, in absolute or relative terms.

The process 700 includes determining that the group composition differs from the target composition in a manner that the group composition does not achieve the target level of diversity (706). The system can store user profiles that describe the attributes, history, medical history, and other characteristics of individuals. The system can compare the group composition with the target composition and determine whether the group composition is within a threshold level of the target composition. This can involve comparing the amounts of participating devices or users in different categories to the respective amounts indicated by the target composition data. This may also include generating a diversity score, for the group as a whole or for different categories of participants, and determining whether the difference is less than a predetermined threshold (e.g., less than 5% different, etc.). In many cases, a difference of at least the predetermined magnitude for any one of various categories or groups (e.g., less than the minimum needed for any of group 1, group 2, group 3, etc.) or for any of the predetermined attributes for which diversity is needed (e.g., age, sex, race, diabetes status, location, mental health history, etc.) can trigger the system to take corrective actions to bring the monitoring group back to the composition needed.

Notably, the system can assess diversity for not only the nominal membership of the monitoring group (e.g., the set enrolled or invited to enroll), but also the set of members actually complying with (or predicted to comply with) the requirements of the monitoring program. For example, for a clinical trial, 1000 participants may be enrolled, but only 850 may be complying with the requirements of the study protocol. These requirements may be for data collection (e.g., completing a survey, acquiring sensor data, etc.) or for other actions separate from data collection (e.g., taking a medication, performing a needed amount of exercise, sleeping according to study guidelines, etc.). As a result, the system can assess the composition and diversity of the set of the 850 complying participants to provide a more accurate indicator of the results of the study. Because compliance and attrition can vary for different participant backgrounds and different participant attributes, the system's assessment of the complying set can provide an early indicator where compliance problems for some groups may decrease the effective diversity among the valid, usable data sets for the study. Participants that do not comply with the requirements may be considered withdrawn from participation for the purposes of the calculation showing the diversity status, even if the participants continue to be enrolled and outreach is made to bring them back into compliance.

In many cases, administrators may enroll an appropriately diverse group of participants at the beginning of the study, but poor quality data collected, incomplete data collection, lack of compliance with participant disproportionately affects participants in one or more groups, which can put the entire research study at risk of cancellation. This, of course, risks wasting all of the resources expended on the study, at the servers of the system as well as at all of the many remote devices. The system can compare the collected data for individual participants with the requirements of the monitoring program they participate in to determine compliance, on a repeated or ongoing basis. This then gives the system the ability to determine the composition and diversity status of the subset of the monitoring group that is complying with the requirements.

The system can be a multi-tenant system that manages many different monitoring programs each with their own requirements for diversity and their own target compositions. The system can monitor the compliance of individuals in each monitoring group with respect to the particular requirements of the corresponding monitoring programs. This allows the system to track, in real time or substantially in real time, whether each monitoring program is meeting its own objectives for composition and diversity.

The process 700 includes selecting one or more actions that are configured to adjust the composition of the group to achieve the target level of diversity (708). When the desired diversity level is not met, the system can identify the participant categories or participant attributes that are underrepresented and determine actions to bring levels for those categories or attributes up to the levels needed to meet the target composition. For example, the system can determine that within a monitoring group, a first category is represented by only 7 participants while the target is 10 individuals. In response, the system can search the user profile database and identify at least three additional individuals in the first category that meet the eligibility criteria for the monitoring program. The identified candidates may then be added to monitoring group (e.g., invited to enroll, added as participants, etc.) to bring the composition and diversity level to the needed state.

The selection of actions can include actions determined to increase the compliance with study requirements for participants in specific categories or with specific attributes that are underrepresented. For example, if 10 participants are needed for category 2, but only 8 out of the 10 enrolled participants in that category are complying with the data collection requirements, then the system can select actions that are calculated to increase the compliance of the two non-complying participants and/or to increase future compliance for all participants in that category. The tables shown in FIG. 4 can be used by the system to identify additional elements to add to the monitoring program to increase compliance (e.g., those associated with positive effect on compliance or at least lower negative effects). The system can also change the manner of communicating with those participants, add additional participants for underrepresented categories or take other actions to improve the diversity level.

The process 700 provides output indicating the selected actions (710). This can include providing the output to a device associated with a researcher or other administrator for the monitoring program, for display in a user interface on the administrator's device. The user interface may include interactive controls, and in response to the controls the system can carry out one or more recommended actions to improve the composition and diversity level for the monitoring group toward the target composition.

In addition, the system may carry out the one or more selected actions, in some cases automatically without requiring specific user input or confirmation. In these cases, the system can carry out adjustments to the monitoring groups (e.g., inviting or enrolling new participants in categories that are underrepresented or are effectively underrepresented when compliance is taken into account), adjusting monitoring program elements for unrepresented groups (e.g., adding or substituting interactions, survey elements, configuration data, etc.), changing the form or content of communications to underrepresented groups, and so on. The system can identify and carry out various types of actions to improve the level of diversity among enrolled and complying members of the monitoring group. This includes providing customized support or interaction to customized for the needs or preferences of different groups. Depending on the participant's background, some may respond better to being provided a credit for taxi service, providing a mobile phone, changing manner of interactions in the study, etc. Similarly, the system may broaden inclusion criteria (e.g., remove limitations to joining the monitoring group) to encompass more of people of the needed backgrounds. Similarly, the system can reduce or remove exclusion criteria to similarly encompass more candidates when needed. As discussed further below, various actions can add additional participants to the cohort from underrepresented groups or may communicate with participants to restore compliance with program requirements. In addition, the system may determine a reason that diversity metrics are out of target ranges, such as identifying one or more factors in the study that are resulting in a bias to enrollment, compliance, or retention. For example, for certain groups, one or more requirements may be disproportionately missed, signaling a need to change that requirement or add additional support for those groups. For each of various potential changes, the system can determine a candidate pool or predicted outcome as if the change was carried out, then select the actions with the best results predicted (e.g., scored as providing the least disruption, highest improvement to diversity, etc.)

In addition to assessing effective level of diversity among participants currently complying with program requirements, the system can assess the diversity among the set of participants projected or predicted to comply with the requirements. This can be useful at the creation of a study, to assess how the requirements of a new study may disproportionally affect the compliance or retention of one group over another. Using the analysis of FIG. 4, the system can identify the elements that have the negative effect and propose substitute elements with better outcome profiles and/or recommend additional elements to compensate for the effect. Similarly, the predictions of compliance rates and retention rates for different groups can be used while a study is ongoing, showing that although current compliance is acceptable, historically the compliance or retention by the end of the study may be worse and may remove the diversity level currently seen.

FIGS. 8A-8B are diagrams that illustrate one or more components of the system 200 and a process for assessing and adjusting the composition of groups for a monitoring program 812 a or the monitoring program 812 a using a set of profiles 804.

The disclosed systems can be used to achieve number benefits. For example, the computer system 210 can provide numerous benefits to realize and improved program monitoring and distribution system.

Notably, other systems often, if not always, fail to start with an appropriate set of subjects to monitor. That is, these systems are incapable of checking if a set of subjects, such as a set of devices or device users, will provide the overall set of data and the diverse context to be able to capture the variety of data required in the monitoring program. Often these systems are simply provided a pre-selected group subjects or randomly select a group of subjects that are bound to produce, or have an unacceptably high likelihood of producing, unviable results or results that fail one or more other goals of the monitoring program.

In contrast, the computer system 210 can address this issue by selecting a group of subjects to invite or enroll in a monitoring program that are predicted to meet the set goals for the monitoring program. In more detail, the computer system 110 may select a diverse group of subjects to enroll or invite to the monitoring program such that the composition of the group meets certain diversity requirements. By including a diverse group of subjects at the outset of the monitoring program, the computer system 210 can at least improve the likelihood of obtaining viable results from the monitoring program. As an example, many medical studies today fail to produce viable results or produce results having severely limited applicability due to a failure to include or maintain a diverse set of participants. Diversity may refer to diversity among various subject attributes, including both demographic and non-demographic attributes.

The computer system 210 may also take into account other attributes of the subjects when selecting a group of subjects to enroll or invite to a monitoring program. For example, the computer system 210 may take into account historical data, trends in the historical data, and, optionally, trends among certain populations to select subjects that are likely to meet the requirements of the study. The historical data or trends may indicate past or anticipated retention rates for subjects or groups of subjects, past or anticipated compliance rates for subjects or groups of subjects, or past or anticipated data quality obtained from subjects or groups of subjects. For example, the historical data may indicate that a particular subset of subjects is likely to have low compliance with a particular requirement of a monitoring program. In response to this determination, the computer system 210 may avoid enrolling or inviting those subjects to the monitoring program.

However, if those subjects are necessary to achieve certain minimum diversity criteria or other goals for the monitoring program, the computer system 210 can modify the elements of the monitoring program for that particular subset of subjects to improve compliance. Modifying the elements may include modifying or removing requirements of the monitoring program, or adding remedial elements. For example, if the particular subset of subjects is determined by the computer system 210 to generally not have access to a vehicle and, as a result, have low compliance with required medical office visits, the system 210 may add taxi credit to a new version of the monitoring program for those subjects as a remedial measure to improve compliance rates for those subjects with respect to office visits.

In selecting subjects at an outset of a monitoring program or determining how to modify the elements of a monitoring program to improve, the computer system 210 may use various profiles that represent categories of subjects. These profiles may be used to determine how particular subjects are likely to respond to certain monitoring program requirements, and, therefore, to determine if they should be enrolled to the monitoring program or if the monitoring program needs to be adjusted for one or more particular groups of subjects. These profiles may additionally or alternatively be used to improve the diversity of a monitored group or to determine if a monitored group has a sufficient diversity. For example, the computer system 110 may identify the profiles corresponding to a monitoring group and use the profiles to determine if there is sufficient diversity, at the outset or predicted diversity at completion of the monitoring program. If diversity is insufficient, the computer system 210 may use the profiles to identify unrepresented or underrepresented profiles, and proceed to enroll or invite subjects from categories represented by those unrepresented or underrepresented profiles.

By selecting at the outset of a monitoring program a group of subjects that will likely provide the overall set of data and the diverse context to be able to capture the variety of data needed for the monitoring program, the computer system 210 is able to significantly reduce computational inefficiencies. Notably, this selection improves the likelihood of obtaining viable results for the monitoring program as a whole, which greatly reduces. As such, the computer system 210 is able to significantly reduce the computational load on the system and the remote devices and the CPU hours of the system and the remote devices.

As shown in FIG. 8A, in response to receiving instructions 802 from the client device 204, the computer system 210 may access the profiles 804 from the database 212 and use the accessed profiles 804 to determine adjustments to make to the monitoring program 812 a or to a group composition selected for the monitoring program 812 a.

The instructions 802 may also include other information. For example, the instructions 802 may indicate an initial monitoring group 808 a. That is, the instructions 802 may include or point to an initial list of devices and/or persons that have been invited to, selected for, or enrolled in the monitoring program.

The instructions 802 may also or alternatively include or otherwise indicate the elements of the monitoring program 812 a. In response to receiving the instructions 802, the computer system 210 may generate the monitoring program 812 a, may select an existing monitoring program that includes the elements in the instructions 802, or may update an existing monitoring program to include the elements in the instructions 802.

The instructions 802 may include data that the computer system 210 uses to initiate a monitoring program 812 a. Specifically, the instructions 802 may include an indication of the specific monitoring program that should be selected for a new monitoring program (e.g., from a list of available monitoring programs).

Alternatively, the computer system 210 may select the initial monitoring group 808 a from a candidate pool of devices and/or users based on the instructions 802. As an example, the instructions 802 may include criteria, such as diversity criteria, for a monitoring program that is to be performed. The computer system 210 may use this criteria to select devices and/or people for the monitoring program to place into the initial monitoring group. As an example, the instructions 802 may include diversity criteria indicating an exact or minimum number of devices or persons there needs to be in the monitoring program that are associated with specific profiles. Specifically, the monitoring instructions 802 may indicate that the monitoring group 808 a must include at least one device assigned to Profile 1, and at least one device assigned to profile 2. Similarly, the instructions 802 may indicate exact, minimum, and/or maximum percentages that represent the population of devices or persons associated with specific profiles in the monitoring group 808 a. For example, the monitoring instructions 802 may indicate that at least 50% of the devices in the monitoring group 808 a should be assigned to Profile 1 and that at least 25% of the devices in the monitoring group 808 a should be assigned to Profile 2.

Each of the profiles in the profiles 804 may correspond to a subgroup of devices and/or persons. Specifically, each profile may correspond to a subgroup or a distinct (e.g., non-overlapping) subgroup of devices and/or persons that share at least one of the same key attributes, similar attributes, or demonstrate the same or similar behaviors. That is, each profile may represent a category of devices and/or candidates. As will be discussed in more detail with respect to FIGS. 9A-9B, the computer system 210 can generate the profiles using previously observed outcomes (e.g., behaviors) and/or attributes attributed candidates for inclusion in the monitoring group. The computer system 210 may generate the profiles using one or more machine learning models, such as one or more clustering algorithms.

The profiles 804 may be previously generated and stored in the database 212. Alternatively, the profiles 804 may be generated or updated in response to the computer system 210 receiving instructions 802 from the client device 204. For example, in response to receiving the instructions, the computer system 210 may generate the profiles 804 or may update the profiles 804 using the most recently available monitoring data.

A profile distribution 806 indicates example information that defines a number of example profiles. The information in the profile distribution 806 may include criteria for determining if a device or person is eligible for assignment to the profile, e.g., inclusion criteria. For example, the criteria for Profile 1 indicates that a subject must be between the ages of 17-25 and belong to Race A to be eligible. The information may also include outcome information (e.g., anticipated behaviors such as retention, compliance, and quality of data) associated with subjects (e.g., devices and/or persons) associated with the profile. As will be discussed in more detail with respect to FIGS. 9A-9B, this outcome information may be determined for each of the profiles by analyzing outcome information associated with previous subjects or current subjects in a category of subjects corresponding to each of the profiles. As an example, 75% of the subjects in Profile 1 do not have access to a vehicle and 25% of the subjects are more likely to respond to SMS message when compared to an email message. The profile distribution may also contain various other data such as a population percentage that the subjects of each of the profiles represent of the total candidate pool. For example, 3.0% of the subjects in the candidate pool are associated with Profile 1. The candidate pool may include all subjects that have previously or are currently enrolled in a monitoring program. Alternatively, the candidate pool may include all active subjects, e.g., those that are currently enrolled in a monitoring program or are available for enrollment in a monitoring program.

In some cases, a subject may be associated (e.g., assigned to) multiple profiles. For example, a subject may meet the inclusion criteria for multiple profiles and, therefore, be associated with the multiple profiles.

In some cases, a subject is associated with only a single profile. For example, if a subject has been assigned to a first profile, they may be prevented from being assigned to a second profile.

The computer system 210 may reassign subjects to different profiles (e.g., determine that they belong to different categories of subjects) over time based on the monitored actions of the subjects. For example, a particular subject may initially demonstrate a smartphone compliance rate below 60% over the first three monitoring programs they participate in, and, as a result, be assigned by the computer system 210 to a first profile of the profiles 804. However, if over the next three monitoring programs they improve their overall compliance rate to 75%, the computer system 210 can reassign the subject to a second profile of the profiles 804.

The computer system 210 may use the profile distribution 806 to adjust the monitoring group (810). The computer system 210 may use the profile distribution 806 to adjust the initial monitoring group 808 a at the outset of the monitoring program for the monitoring program 812 a. In more detail, the computer system 210 may use the profile distribution 806 to identify categories of devices or persons that are underrepresented in the initial monitoring group 808 a. For example, the computer system 210 may use the profile distribution 806 and the initial monitoring group 808 a to determine that a device corresponding to Profile 5 should be added to the monitoring group 808. The computer system 210 may make this determination based on diversity reasons, e.g., in order to have at least one device from each profile or from each profile in one or more subsets of profiles. The computer system 210 may make this determination based on one or more determinations or predications. For example, the computer system 210 may use the profile distribution 806 to select a Profile 5 device based on the higher study completion rate of the profile 5 subjects in order to boost efficiency, increase likelihood that study will be successful (e.g., if an analysis of the initial monitoring group reveals that there is a higher than acceptable chance of study failure), etc. As another example, the computer system 210 may determine to add a Profile 5 device based on the percentage of the candidate pool. Specifically, the computer system 210 may add devices associated with unrepresented or underrepresented profiles for each profile that corresponds to at least threshold percent (e.g., 3%) of the candidate pool. Accordingly, the computer system 210 may determine to add a Profile 5 subject to the monitoring group 808 based on the Profile 5 population meeting the threshold percent.

After determining to add a Profile 5 subject to the monitoring group 808, the computer system 210 may automatically enroll a Profile 5 subject in the monitoring program, or may generate and send an invitation to a Profile subject to enroll in the monitoring program. As an example, the computer system 210 may take into consideration one or more factors to determine which subject to enroll or invite to the monitoring program. These factors may include, for example, the retention rates associated with the subject, the compliance rates associated with the subject, quality of data previously obtained from the subject or otherwise associated with the subject, the experience of the subject (e.g., number of monitoring program the subject has previously participated in), the activity level of the subject (e.g., how recent the subject has participated in a monitoring program), invitation acceptance rate of the subject, trends in factors (e.g., trends in the retention rates, compliance rates, activity level, etc. of the subject), etc. For example, a subject that has participated in at least one monitoring program over the last year and has a retention rate of 78% may be selected by the computer system 210 for enrollment over a subject that has not participated in at least one monitoring program over the last year and/or has a retention rate less than 70% despite both subjects corresponding to Profile 5.

After determining to add a Profile 5 subject to the monitoring group 808, the computer system 210 may generate and send a recommendation to the researcher 202 to enroll a Profile 5 subject or to send an enrollment invitation to a Profile 5 subject. The recommendation may include multiple subjects recommended by the computer system 210. The multiple subjects may be arranged in an order that corresponds to a recommendation order, such that the subject that the computer system 210 recommends most is shown first or highest in a list of subjects. The computer system 210 may wait to receive a response from the researcher, e.g., wait to receive a response from the client device 204, to enroll or invite one or more of the recommended subjects. Alternatively, the computer system 210 may wait for a predetermined amount of time after transmitting the recommendation to the client device 204. If a response is not received by this point, the computer system 210 can automatically enroll a Profile 5 subject in the monitoring program, or generate and send an invitation to a Profile subject to enroll in the monitoring program.

The computer system 210 may also use the profile distribution to adjust the monitoring program 812 a (814). That is, the computer system 210 can determine adjustments to make to the monitoring program 812 a using information in the profile distribution 806 corresponding to profiles associated with subjects in the monitoring group 808. The adjustments may include one or more changes to elements of the monitoring program 812 a. As an example, these adjustments may include one or more of the following: modifications to the inclusion criteria for the monitoring program; modifications to the exclusion criteria for the monitoring program; modifications to the type, source, schedule, or frequency of data collection; modifications to the type, source, schedule, or frequency of data requests; modifications to monitoring program events or other requirements; modifications to communication methods, content, schedule, and/or frequency; and/or the addition of support features.

The adjustments may correspond to particular profiles of the profiles 804. For example a first set of adjustments may be particular to the Profile 1 subjects while a second set of adjustments may be particular to the Profile 2 subjects. That is, the computer system 210 may customize the monitoring program 812 a for one or more profiles, e.g., based on the information contained in or associated with the profiles as indicated by the profile distribution 806. As shown, the computer system 210 uses the profile distribution 806 to customize the monitoring program 812 for the Profile 1 devices and the Profile 2 devices. Specifically, for the Profile 1 devices, the computer system 210 adjusts the monitoring program 812 to provide a weekly transportation credit to those Profile 1 devices based on, for example, the profile distribution 806 indicating that 75% of Profile 1 subjects do not have a vehicle and the monitoring program 812 requiring weekly medical office visits. Similarly, the computer system 210 adjusts the monitoring program 812 for the Profile 2 devices to update the communication channel from email to voice call based on the profile distribution 806 indicating that Profile 2 subjects are 60% more likely to respond to voice call over email and SMS message.

The computer system 210 can automatically make the adjustments to the monitoring program 812 or, alternatively, can generate a recommendation that includes the proposed adjustments. The computer system 210 may transmit the recommendation to the client device 204.

The computer system 210 may update the monitoring program for one or more groups of devices to improve efficiency and/or the likelihood of success of the monitoring program. That is, the computer system 210 may update the monitoring program 812 a to improve the outcomes of the monitoring program (e.g., improve likelihood of subject compliance, compliance rates, retention rates, and/or quality of data obtained).

The adjustments to the monitoring program 812 may be temporary for the particular monitoring program. Alternatively, the adjustments to the monitoring program 812 may be permanent so as to update the default elements of the monitoring program.

FIG. 8B illustrates the adjustments made by the computer system 210 to the monitoring group 808 and to the monitoring program 812 for different categories of subjects. As shown, the computer system 210 has updated the monitoring group 808 a which included a first subgroup 822 of devices that belong to a first category of subjects (e.g., subjects that correspond to Profile 1 of the profiles 804) and a second subgroup 824 of devices that belong to a second category of subjects (e.g., subjects that correspond to Profile 2 of the profiles 804) to the monitoring group 808. The monitoring group 808 b includes the first subgroup 822 of devices, the second subgroup 824 of devices, and a third subgroup 826 of devices that belong to a third category of subjects (e.g., subjects that correspond to Profile 5 of the profiles 804).

Based on determinations made using the profile distribution 806 and the monitoring group 808 b, the computer system 210 generates customized monitoring programs 812 b, 812 c, and 812 d for each subgroup of devices 822, 824, and 826, respectively. The changes 814 indicate the changes that the computer system 210 made to the initial monitoring program 812 a to generate each of the customized monitoring programs 812 b, 812 c, and 812 d. The changes 814 made to the initial monitoring program 812 a for the different subgroups of subjects may be made to improve predicted outcomes for the study. For example, the changes 814 may be made by the computer system 210 in an effort to improve retention of the subjects in the different subgroups, improve compliance with the requirements of the monitoring program, improve the likelihood of obtaining a minimally acceptable amount of data (e.g., to get results from the monitoring program that are statistically relevant, or that meet some other viability measure), improve the likelihood of obtaining at least a minimum level of data quality (e.g., to get results from the monitoring program that are statistically relevant, or that meet some other viability measure), etc.

For the subgroup 822 subjects, the changes 814 include a change 832 a to message frequency, and an addition of an assistive element 832 b to the monitoring program 812 to provide transportation credit 832 b. As an example, Profile 1 of the profiles 804 may indicate that the Profile 1 subjects respond better to more frequent communication (e.g., may indicate higher compliance and/or retention rates with more frequent event reminders). The computer system 210 may use this information to increase the default message, e.g., from weekly to daily.

For the subgroup 824 subjects, the changes 814 include a change 834 to the communication channel. As an example, Profile 2 of the profiles 804 may indicate that the Profile 2 subjects demonstrate higher compliance when voice calls are used over other communications channels. The computer system 210 may use the information in Profile 2 to change the default communication channel, e.g., from email to voice call.

For the subgroup 826 subjects, the changes 814 include a change 836 to the communication channel. As an example, Profile 5 of the profiles 804 may indicate that the Profile 5 subjects have significantly lower retention rates when monitoring programs require the subjects to submit test results more than two times a day when compared to the retention rates for Profile 5 subjects when they are required to submit test results two times a day or less frequently. The computer system 210 may use the information in Profile 5 to modify the glucometer reading requirement, e.g., form requiring three readings per day to two readings per day.

FIGS. 9A-9B are diagrams that illustrate one or more components of the system 200 an example system for generating profiles.

As shown in FIG. 9A, the computer system 210 may generate profiles using outcome data 940, program data 940, and/or subject data 950 stored in the database 212.

The monitoring outcome data 940 may include compliance data 932 (e.g., that indicates previously observed compliance rates for past subjects), retention data 934 (e.g., that indicates previously observed compliance rates for past subjects), and data quality data 936 (e.g., that indicates the quality of data that was obtained from past subjects). The monitoring outcome data 930 may be organized based on the individual subjects. For example, different portions of the compliance data 932, the retention data 934, and the data quality data 936 may correspond to a specific subject. The portion of the compliance data 932 associated with the specific subject may indicate, for example, the overall compliance of the subject across all requirements for all previous monitoring programs or all monitoring programs that are sufficiently recent, the compliance of the subject for particular requirement categories (e.g., smartphone compliance, office visit compliance, etc.) across all previous monitoring programs or all monitoring programs that are sufficiently recent, etc.

The program data 940 may describe the requirements of each monitoring program. For example, the program data 940 may include the default elements of each monitoring program. As was described in more detail with respect to FIGS. 8A-8B, these elements may be removed or modified based on profile data associated with the monitored group. Similarly, elements may be added to monitoring programs based on profile data associated with the monitored group.

More specifically, the program data 940 may include instructions to acquire or request specific data or types of data from subjects, instructions for subjects to perform particular actions, instructions as to the channel of communication and/or the frequency of communication between the computer system 210 and the subjects, instructions to provide accommodations (e.g., taxi credits), etc.

The program data 940 may also include various data packets corresponding to different monitoring programs. These data packets may include, for example, installation files for programs that are to be run on devices to be monitored in a monitoring program.

The subject data 950 may include the attributes, history, behavior, and other tracked data for monitored subjects. As an example, the attribute information in the subject data 950 may include demographic as well as non-demographic information for each of the monitored subjects (e.g., previously monitored subjects or currently monitored subjects), such as race, ethnicity, age, sex, residential area (e.g., city, state, country, etc.), type of residential area (e.g., urban, suburban, or rural), medical conditions, surgeries (e.g., type of surgery and date of surgery), prescriptions, etc. The history information may include information indicating the past monitoring programs that the subject has participated in or completed, and/or the past monitoring programs used during those sessions. The behavior information may indicate the particular subject's observed responses or compliance with certain monitoring program requirements (e.g., elements). As an example, the behavior information may indicate that the subject is 50% less likely to be retained if the monitoring program requires him to make weekly doctor visits. The behavior information may also include or point to portions of the compliance data 932, the retention data 934, and/or the data quality data 936 associated with that particular subject.

In generating the profiles, the computer system 210 may first use one or more machine learning models to cluster subjects from a set. The set may include, for example, subjects that have previously participated in a monitoring program, have previously completed a monitoring program, are currently participating in a monitoring program, have previously participated in a monitoring program that was held sufficiently recent, or have previously completed a monitoring program that was held sufficiently recent. The computer system 210 may use a clustering model 908 to generate different clusters of subjects or eligible subjects based on certain input.

As shown, the computer system 210 may provide one or more of key attributes 902 or outcomes 904 as input to the clustering model 908. The computer system 210 may optionally provide cluster parameters 906 as input to the clustering model 908. In addition, the clustering model 908 may have access to the monitoring outcome data 930, the program data 940, and/or the subject data 950, or may be provided the monitoring program data 930, the program data 940, and/or the subject data 950 as input.

The key attributes 902 may include a list of types of attributes that the clustering model 908 can use to cluster the subjects. For example, the key attributes 902 may include a list of attribute types that are found in the subject data 950. The key attributes 902 may be selected by the researcher 202, may correspond to a particular monitoring program selected for a monitoring program, and/or may be determined by the computer system 210. For example, a researcher 202 may indicate, for diversity purposes, that the key attributes 902 should include race, ethnicity, and medical conditions of the subjects. Based on this, the clustering model 908 may cluster the subjects based on the key attributes 902 or based in part on the key attributes 902 (e.g., clustering model 908 may also take into account the outcomes 904 or other data in the subject data 950).

The key attributes 902 may additionally or alternatively include a subset of the subject data 950. That is, the key attributes 902 may include a portion of the attribute information in the subject data 950 corresponding to the type of attribute selected. The outcomes 904 may include a list of types of outcomes that the clustering model 908 is to cluster the subjects based on. For example, the outcomes 904 may include a list of outcome types that are found in the monitoring outcome data 930. The outcomes 904 may be selected by the researcher 202, may correspond to a particular monitoring program selected for a monitoring program, and/or may be determined by the computer system 210. For example, a researcher 202 may indicate that subjects should be clustered based on smartphone compliance during monitoring programs. The outcomes 904 may additionally or alternatively include all or a portion of the monitoring outcome data 930. For example, the outcomes 904 may include all or a portion of the compliance data 932, the retention data 934, or the data quality data 936. The cluster parameters 906 may include additional criteria for the clusters. For example, the cluster parameters 906 may specify a minimum cluster size, a maximum cluster size, the number of clusters, a minimum number of clusters, a maximum number of clusters, etc.

The clustering model 908 uses the key attributes 902, the outcomes 904, and/or the cluster parameters 906 to generate the clusters 910, 912, 914, 916 a. Each of the clusters contain at least one subject of the subject pool. As an example, the key attributes 902 may include medical conditions or a particular set of medical conditions. Based on this, the clustering model 908 may generate the clusters 910, 912, 914, and 916 a such that each includes subjects that generally have the same or similar medical conditions. As another example, the clustering model 908 may additionally or alternatively cluster subjects in the subject pool based on outcome data associated with the subjects. That is, the clustering model 908 may group subjects that have the same or similar monitoring program compliance rates, monitoring program retention rates, data quality, or health outcomes (e.g., degree of recovery or management of a disease, occurrence of side effects, etc.).

Clustering by the clustering model 908 may be performed in a number of ways. As discussed above, clustering may be primarily based on the attributes of the subjects, such as demographic and/or non-demographic information stored for subjects as part of the subject data 950. In this example, the clustering model 908 may generate clusters of subjects where each cluster includes a group of subjects that have a number of attributes in common (e.g., same ethnicity, same race, same medical conditions, etc.), have a number of similar attributes (e.g., similar or otherwise related medical conditions that fall into the same category of medical conditions, a height that falls within a particular height ranged determined by the clustering model, etc.), have a number of the key attributes 902 in common, and/or have a number of similar attributes of the key attributes 902. In more detail, the clustering model 908 may cluster subjects based on subjects in each of the groups having the highest number of attributes in common or the highest number of the key attributes 902 in common for that particular cluster when compared to the subjects assigned to other clusters.

Another way that the clustering model 908 can generate the clusters 910-916 is by clustering based on the outcome data 930. For example, the clustering model 908 can generate clusters of subjects based on those that perform similarly. In more detail, the clustering model 908 may generate the clusters 910-916 that each correspond to different groups of subjects that have the same or similar retention rates, the same or similar study completion rates, the same or similar compliance rates (e.g., smartphone compliance, office visit compliance, etc.), etc. Accordingly, the computer system 210 can use profiles generated from these clusters to predict how a subject is likely to perform in a current monitoring program if they meet the eligibility criteria for the profiles. Determining on the number of subjects that are assigned to the different profiles, the computer system 210 may determine that more subjects need to be enrolled in the current monitoring program (e.g., due to the profiles indicating a low retention rate and/or completion rate), ideally those that correspond to a profile which indicates a high completion rate, retention rate, and/or compliance rate.

In assigning subjects to clusters, the clustering model 908 may determine a set of attribute values or ranges that serve as criteria for the clusters. For example, the clustering model may determine that the second cluster 912 requires that the subjects belong to Race B, be over thirty-five years old, and have diabetes. Based on subjects O and V meeting these criteria, the clustering model 908 may determine that the second cluster 912 includes subjects O and V. This criteria may be used to generate a profile corresponding to the second cluster 912. For example, the same criteria may be used as eligibility criteria for determining if a corresponding profile is applicable to a subject in a current monitoring program, and/or the criteria may be modified (e.g., by an administrator) before being used as eligibility criteria for determining if a corresponding profile is applicable to a subject in a current monitoring program. Clustering in this manner can be used to achieve clusters of subjects that can be used to meet certain diversity criteria. That is, clusters of subjects can be formed where each cluster includes subjects having particular attributes. Accordingly, in conducting a new monitoring program, profiles generated from these clusters can be used to determine if certain diversity criteria is being met or may be used a substitute for diversity criteria. For example, if it is determined that no subjects in a current monitoring program belong to a first profile corresponding to the cluster 910, then additional subjects should be invited to join the monitoring program in order to improve the diversity of the monitoring program and, thereby, improve the applicability of the results and/or improve the likelihood of the results being valid.

Alternatively, the clustering model 908 may generate a set of attribute values or ranges after determining the clusters from the attributes of the subjects in the determined clusters. For example, after generating the clusters 910-916, the clustering model 908 may determine for each of the clusters eligibility criteria for the cluster using the attributes of subjects assigned to each of the clusters. In more detail, for the second cluster 912, the clustering model 908 may access a subject of the subject data 950 corresponding to the subjects O and V based on the subjects O and V having been assigned to the cluster 912. The clustering model 908 may use this subset of data to generate eligibility criteria for the cluster 912 and/or eligibility criteria for a profile based on the cluster 912. The types of attributes used to generate the criteria may be those that are indicated in the key attributes 902. For example, if the key attributes 902 indicate that clustering should take into account the ethnicity of the subjects, then the ethnicity of the subjects assigned to the cluster 912 should differ from the ethnicity of the subjects assigned to the other clusters. Accordingly, the computer system 210 can access from the subject data 950 the ethnicity data corresponding to the Subjects O and V and use that data to generate the criteria. Additionally or alternatively, the clustering model 908 may determine what attributes of the subjects assigned to the cluster 912 are unique with respect to the other clusters. For example, the clustering model 908 may determine that the cluster 912 is the only cluster to include subjects over thirty-five years old. Accordingly, the clustering model 908 may determine that the criteria corresponding to the cluster 912 should include a requirement of a subject being over thirty-five years old.

Thresholds or other boundaries may be optionally applied to one or more of the generated clusters (918). For example, thresholds or other boundaries set by the researcher 202 may be applied to one or more of the clusters 910, 912, 914, and 916 a. Applying the thresholds or other boundaries can result in removing clusters, splitting a cluster into two or more new clusters, removing a subject from a cluster or otherwise dissociating the subject with the cluster, etc. As an example, the threshold or other boundaries may include inclusion criteria for the clusters generated by the computer system 210 or set by the researcher 202. The computer system 210 may apply this inclusion criteria to the clusters. As an example, the computer system 210 may apply an inclusion criterion that all subjects in the cluster 916 a must be older than 30 years of age. As such, the computer system 210 may update the cluster 916 a to disassociate any subjects that were 30 years of age or younger.

The subjects associated (e.g., assigned to) each of the clusters 910, 912, 914, and 916 may be considered members of their respective clusters. A table 920 includes example members of the first cluster 910, the second cluster 912, and the third cluster 914.

As shown in FIG. 9B, the computer system 210 may use the different clusters to generate profiles. In more detail, the computer system 210 may generate one or more profiles from the clusters. Additionally or alternatively, the computer system 210 may determine that one or more of the clusters are not eligible for profile generation, e.g., due to not meeting eligibility requirements (e.g., minimum member size; minimum subject population representation; minimum diversity level; etc.). For example, the computer system 210 may analyze each of the clusters to determine if they meet certain requirements for profile generation. As an example, a cluster may only be eligible for profile creation if it represents a threshold percentage of the subject pool or eligible subject pool.

In generating the profiles, the computer system 210 may analyze each of the previously determined clusters. For example, the computer system 210 may perform a first analysis on the cluster 910. In this analysis, the computer system 210 may determine if the cluster 910 meets the requirements for a profile (922 a). Here, the profile requirements include a requirement that the number of members in the cluster meet a member threshold (e.g., at least three members, at least ten members, at least one-hundred members, etc.). The computer system 210 may compare the number of members in the cluster 910 to the member threshold to determine that the member threshold is met, and, therefore, that the cluster 910 meets the profile requirements.

After determining that a cluster meets the profile requirements, the computer system 210 can analyze the characteristics of the cluster's members (924). For example, the computer system 210 may obtain the demographic and non-demographic corresponding to the members of the cluster 910. For example, the computer system 210 may generate a table 926 from subject data obtained from the database 212. After obtaining this information, the computer system 210 may use the information to identify shared or common attributes among the members (e.g., race, religion, ethnicity, sex, residence area, level of education, health conditions, prescriptions, past surgeries, etc.), calculate various statistics for the members (e.g., percentage of members that live in a rural area, percentage of members that have access to a vehicle, etc.), and determine likelihoods of particular outcomes (e.g., likelihood of completing a study, meeting a minimum compliance rate, providing sufficient data for monitoring program/session requirements, etc.) and behaviors (e.g., smartphone compliance, attending medical appointments, responding to reminders, etc.).

The computer system 210 may also identify trends or patterns in the obtained subject data. For example, the computer system 210 may recognize that those cluster 910 members that reside in rural areas tend to have similar and distinct smartphone compliance rates when compared to the cluster 910 members that reside in urban areas.

The computer system may proceed to generate profiles 960 from the cluster based on the analysis results (928). For example, the computer system 210 may use the shared or common attributes among the members to generate inclusion criteria for the profile. Similarly, the computer system 210 can include the calculated statistics and determined likelihoods in the profile(s) corresponding to the cluster 910.

In generating profile(s) from the cluster, the computer system may generate multiple from the cluster. For example, the computer system 210 may generate a first profile corresponding to a first subset of the cluster 910 members and a second profile corresponding to a second subset of the cluster 910 members. The subset of members may be determined by the computer system 210, e.g., based on identified trends or patterns in the subject data. Alternatively, the computer system 210 may determine the subsets based on input from the researcher 202. For example, the researcher 202 may indicate one or more attributes that must be shared among profile members. In more detail, the input from the researcher 202 may indicate that all profile members must share the same residence type. Based on this, the computer system 210 may split the cluster 910 members into a first subgroup corresponding to a first profile for members that reside in urban areas, and a second subgroup corresponding to a second profile for members that reside in rural areas.

If the computer system 210 generates multiple profiles from a single cluster, the computer system 210 may analyze each of the multiple profiles. The computer system 210 may do this to (i) determine if the profiles meet the profile requirements (e.g., computer system 210 may eliminate one of the multiple profiles if it does not meet the member threshold) and (ii) analyze the characteristics of the profile members. The computer system 210 may use the determined shared or common attributes among the profile members to generate inclusion criteria for the profile. Similarly, the computer system 210 may include the resulting statistics and likelihoods in the profiles.

In some cases, in generating the profiles, the computer system 210 uses the clustering model 908 to perform another round of clustering. For example, the computer system 210 may use the clustering model 908 to perform another round of clustering based on a different set of attributes (e.g., a set of attributes other than the key attributes 902) and/or based on the outcomes 904. The computer system 210 may perform this second round of clustering before analyzing the clusters, such that the resulting clusters are analyzed to determine if they meet the cluster requirements.

After generating the profiles 960, the computer system 210 may perform an analysis on each of the profiles. In performing the analysis, the computer system 210 may analyze the effect of study parameters on the profile members (962). For example, the computer system 210 may use the subject data 250 to calculate the effects of different parameters (e.g., monitoring program requirements such as required tests that must be performed by subjects, frequency of tests that must be performed subjects, office visits that subjects must attend, morning office visits, afternoon office visits, etc.) on the outcomes of subjects (e.g., retention rates, compliance rates, sufficient data quality rates, etc.). As an example, based on this analysis, the computer system 210 can determine that Profile 1 subjects are 35% more likely to comply (e.g., respond to) with SMS message over communications sent by email. Similarly, based on this analysis, the computer system 210 can determine that the retention rate of Profile 1 subjects is reduced by 8% when subjects are required to attend weekly office visits.

In some cases, the computer system 210 may analyze the communication behavior of the members of a profile (964). This analysis may be a separate analysis from analyzing the effect of study parameters, or may be part of that analysis. In analyzing the communication behavior, the computer system 210 may determine the preferred communication channel, communication frequency, communication time, communication content, communication vocabulary (e.g., word choice), or communication sentence structure for the profile's members. The computer system 210 may further determine the effects of particular communication attributes (e.g., channel, frequency, time sent, etc.) on the outcomes of the profile's members, e.g., when compared to other communication attributes. For example, the computer system 210 may determine that the Profile 1 subjects prefer communication by SMS text message over email. The computer system 210 may analyze the subject data 250 to determine that, when compared to email, the Profile 1 subjects are 35% more likely to respond to SMS text message.

The computer system 210 may update profiles over time using monitored data. For example, the computer system 210 may reanalyze the effects of study parameters on profile members using updated subject data. The computer system 210 may perform this analysis after a triggering event is detected, such as the passing of a predetermined amount of time, after a threshold amount of monitored data is collected, after a monitoring program ends, etc. Similarly, the computer system 210 may use the clustering model 908 to cluster subjects after a triggering event is detected (e.g., after a predetermined amount of time has passed, after a threshold amount of monitored data is collected, after a threshold number of new subjects have appeared/joined, etc.). The computer system 210 may proceed to analyze the clusters in the manner described above. Additionally or alternatively, the computer system 210 may avoid additional analysis of a cluster (e.g., to improve efficiency, reduce processor load, increase processing speed, etc.) if it is determined that the members for a particular cluster match the membership for a previously determined cluster.

The computer system 210 may reassign users to different profiles overtime. For example, the computer system 210 may reassign users using the clustering model 908 (e.g., if the output of the model indicates that the users now belong to a cluster not associated with their current profile(s)). As another example, the computer system 210 may automatically assign users to profiles and/or reassign users to different profiles if the corresponding subject data indicates that they (i) meet the inclusion criteria for one or more profiles that they are currently not assigned to, and/or (ii) they no longer meet the inclusion criteria for one or more profiles that they are currently assigned to (e.g., current age indicates that they are no longer in a particular age range required for a first profile).

FIG. 10 is a diagram that illustrates an example table 1000 that includes impact scores corresponding to different clusters 1002 and 1004. In more detail, the table 1000 indicates the impact that different study parameters and other elements are anticipated to have on different clusters of subjects 1002 and 1004.

The impact scores may indicate a quantified impact on one or more outcomes of a monitoring program, such as the retention of subjects, compliance of subjects (e.g., overall compliance, or compliance with particular requirements), data quality, etc. An impact on retention of subjects may indicate an anticipated increase or decrease to the retention rate for a group of subjects (e.g., based on stored historical data) that is attributable to one or more particular study parameters or other elements. As another example, an impact on retention may indicate an anticipated increase or decrease to the likelihood of a minimum number or percent of subjects being retained by the end of the monitoring program with respect to those subjects in the cluster or assigned to the corresponding profile.

Similarly, an impact on compliance of subjects may indicate an anticipated increase or decrease to the compliance rate for a group of subjects (e.g., based on stored historical data) that is attributable to one or more particular study parameters or other elements. As another example, an impact on compliance may indicate an anticipated increase or decrease to the likelihood of a minimum acceptable compliance rate for the monitoring program (e.g., for study data viability) with respect to those subjects in the cluster or assigned to the corresponding profile.

An impact on data quality may indicate an anticipated increase or decrease to the data quality (e.g., determined based on whether required or requested data was received, the accuracy of data received, the accuracy of the sensor(s) used to acquire the data, the time spent by a subject to produce the data (e.g., did subject spend the time to read and accurately respond to a set of questions), the quantity of data received, the response time of receiving data after requested, etc.). As another example, an impact on data quality may indicate an anticipated increase or decrease to the likelihood of a minimum acceptable data quality (e.g., for study data viability) being achieved (e.g., by the end of the study) with respect to those subjects in the cluster or assigned to the corresponding profile.

The impact scores may correspond to percentages. For example, a “−1” impact score on retention may indicate that it is anticipated that about 10% (e.g., a value rounded to the nearest decimal place and assigned a positive or negative indicator based on the effect of the parameter) of the cluster 1 subjects will not be retained during the study session (e.g., based on historical data of the subject data 250). The percentages may be particular to the effect. For example, a “+2” impact score on effect of data quality may indicate that it is anticipated that there will be about a 15% increase in data quality as a result of the inclusion of the particular study parameter or element in a monitoring program. In contrast, a “+2” effect on retention may indicate that it is anticipated that there will be about a 10% increase in retention as a result of the inclusion of the particular study parameter or element in a monitoring program.

The impact scores may correspond to percentage ranges. For example, a “−1” may indicate a decrease of 10% or less, a “−2” may indicate a decrease of 25% or less, and a “−3” may indicate a decrease of greater than 25%. The percentage ranges may be set by the researcher 202 or may be determined by the computer system 210. For example, an impact of “±1” may indicate small impact as defined by the researcher 202, an impact of “±2” may indicate medium impact as defined by the researcher 202, and an impact of “±3” may indicate a large impact as defined by the researcher 202. As previously mentioned, the impact scores may be particular to the effect such that percentage ranges differs between effect on compliance, effect on retention, and/or effect on data quality.

The computer system 210 may calculate the impact scores in the table 1000. For example, the computer system 210 may calculate the impact scores when analyzing the effects of study parameters on profile members (962) described above with respect to FIG. 9B.

Instead of impacts on clusters of subjects, the table 1000 may additionally or alternatively indicate the anticipated impact of different study parameters and other elements on different groups of subjects that correspond to particular profiles. For example, the impact information in the table 1000 corresponding to the cluster 1002 may actually correspond to Profile 1 of the profiles 960. Similarly, the impact information in the table 1000 corresponding to the cluster 1004 may actually correspond to Profile 2 of the profiles 960.

FIG. 11 is a diagram that illustrates an example profile 1102.

As shown, the profile 1102 includes a number of tables 1104 a, 1104 b, and 1104 c that indicate the impact of different study parameters or other elements on the compliance, retention, and data quality. The profile 1102 includes a first table 1104 a that indicates the anticipated impact of different study parameters and/or other elements on the compliance, retention, and data quality during a given monitoring program. For example, if a monitoring program requires weekly in-person visits, the computer system 210 can anticipate a small reduction to Profile 1 subject retention (e.g., compared to their typical retention rate), a moderate reduction to Profile 1 subject compliance (e.g., compared to their typical compliance rate), and a significant improvement to Profile 1 subject data quality (e.g., compared to their typical data quality provided).

The profile 1102 also includes a second table 1104 b that indicates the anticipated impact of different communication types on the compliance, retention, and data quality during a given monitoring program, and a third table 1104 c that indicates the anticipated impact of different communication frequencies on the compliance, retention, and data quality during a given monitoring program.

The information in the tables 1104 a, 1104 b, and 1104 c can be determined by the computer system 210. For example, the computer system 210 may calculate the impact scores of the various study parameters and other elements when it analyzes the effect of study parameters on profile members (962) described in more detail above.

The profile 1102 also includes a fourth table 1106 that indicates the inclusion criteria to determine if subjects belong to a category of subjects represented by the profile 1102. The inclusion criteria may include both demographic and non-demographic information. For example, the inclusion criteria in the fourth table 1106 may require that all Profile 1 subjects be between the ages of 17-25 but also require them to being diagnosed with the medical condition, diabetes.

The profile 1102 also includes a fifth table 1108 that includes the determined behaviors and attributes for those subjects associated with the profile 1102. For example, the computer system 210 may determine these behaviors and/or attributes using the subject data 250. The behaviors may include, for example, an overall retention rate (e.g., study completion rate), an overall compliance rate, one or more particular compliance rates (e.g., corresponding to particular requirements of a monitoring program, such as a smart phone compliance rate if a monitoring program requires subjects to use a smart phone and/or collect sensor data with a smart phone), etc. The attributes may include a subset of attributes that are determined to be unusual (e.g., significantly deviate from subject averages). For example, it may be unusual that only 25% of Profile 1 subjects have access to a vehicle (e.g., where 55% of subjects on average have access to a vehicle).

FIG. 12 is a flowchart diagram that illustrates an example process 1200 for customizing monitoring programs involving remote devices. The process 1200 may be performed by one or more computers, such as the computer system 210 shown in various figures including FIG. 2. The operations of the process 1200 may be distributed among one or more servers, one or more client devices, and/or other computing systems. For example, the operations of the process 1200 may be performed by a management and distribution system, such as the system 210, that includes one or more servers, one or more client devices, and/or other computing systems.

The process 1200 includes extracting at least one of (i) subject attribute data or (ii) subject outcome data (1202). Subjects may include at least one of devices or users. For example, a group of subjects selected for a monitoring program may include a diverse set of devices, such as a set of different smartphones. Prior to monitoring the subject devices, the computer system 210 may distribute software for the monitoring program to the subject devices. The subject devices may proceed to install the software. The software may specify the collection of data using sensors of the subject devices or devices connected to the subject devices (e.g., according to a set schedule or in response to receiving particular requests from the computer system 210), provide for a channel of communication between the computer system 210 and the subject devices (e.g., a secure channel of communication, such as an encrypted communication channel), a user interface through which one or more users of the subject devices can interact (e.g., to respond to messages or notifications sent to the subject devices from the computer system 210), etc.

Subjects may additionally or alternatively include a set of users. The users may include users that have participated in one or more previous monitoring programs or monitoring programs. In some cases, the users may include users that are new and have not participated in any monitoring programs or monitoring programs. Prior to monitoring the subjects, the computer system 210 may distribute software for the monitoring program to remote devices that the subject users have access to. The software may be installed in these remote devices and specify, for example, the collection of data of the subject users using sensors of the devices (e.g., according to a set schedule or in response to receiving particular requests from the computer system 210), provide for a channel of communication between the computer system 210 and the devices (e.g., a secure channel of communication, such as an encrypted communication channel), a user interface through which one or more of the subject users can interact with the devices (e.g., to respond to messages or notifications sent to the subject devices from the computer system 210), etc.

Where the subject is a device, extracting subject attribute data describing characteristics of a subject may include, for example, extracting an ID assigned to the subject device, a manufacturer of the device, a model of the device, a software version running on the device, a CPU speed of the device, a memory size of the device, an indication of sensors installed in the device (e.g., fingerprint scanner, GPS unit, Lidar, accelerometer, etc.), etc. Similarly, where the subject is a user, extracting subject attribute data describing characteristics of the subjects may include, for example, extracting a name of the user, an ID assigned to the user, demographic information of the user (e.g., age, race, ethnicity, gender, marital status, income, education, employment, residential state, etc.), non-demographic information of the user (e.g., past surgeries, medical conditions, genetics, lifestyle patterns, environmental factors, access to health care, etc.), an indication of what devices and/or sensors that user has access to, etc. As will be described in more detail below, the computer system 210 may use the attribute data of the subjects to categorize the subjects. As an example, with respect to FIG. 9A, the subject attribute data may be part of the subject data 950.

Extracting the subject outcome data can include extracting subject outcome data including results from monitoring programs that involved the subjects. The results may include, for example, compliance data for the subjects, retention data for the subjects, and data quality data for the subjects. As an example, with respect to FIG. 9A, the subject outcome data may include the compliance data 932, the retention data 934, and the data quality data 936. The computer system 210 may determine the subject outcome data using, for example, monitored results of subjects during the past monitoring programs. The monitored results may be stored, at least initially, as part of the subject data 950. The computer system 210 may extract the relevant information from the subject data 950, such as response times, response content, sensor data, etc., to determine the subject outcome data.

The compliance data may include, for example, compliance rates such as an overall compliance rate for each of the subjects or for each category of subjects with respect to monitoring program requirements of past monitoring programs and/or monitoring programs that the subjects have participated in. However, the compliance data may also or alternatively include compliance rates for particular areas, such as the subjects' compliance with device usage (e.g., smartphone compliance, blood pressure device compliance, heart rate monitor compliance, etc.), with responsiveness (e.g., does the subject on average respond within thirty minutes of receiving a message or notification, within one hour of receiving a message or notification, within one day of receiving a message or notification, etc.; does the subject consistently provide required test results; does the subject consistently perform required tasks; etc.), with office visits (e.g., medical visits scheduled as part of the monitoring program), etc. In determining a compliance rate for a subject, the computer system 210 may average all of the relevant compliance rates (e.g., overall compliance rate, or subject-specific compliance rate) for the subject across their past monitoring programs.

The retention data may include, for example, a retention rate for each of the subject or for each category of subjects over their past monitoring programs sessions. As an example, the computer system 210 may determine a retention rate for each subject using the number of monitoring programs the subject has previously participated in, and the number of monitoring program the subject successfully completed. As an example, the computer system 210 may determine that a subject was not retained in (e.g., did not complete) a particular monitoring program if they stopped responding, if their compliance rate(s) fell below threshold compliance rate(s), if they failed to perform one or more tasks (e.g., perform tests, upload tests results, attend medical office visits, fill out surveys, meet dietary restrictions, perform required exercises, etc.), or if they indicated that they were withdrawing from the monitoring program. The computer system 210 may keep track of the subject retention rates as part of the retention data 934. The computer system 210 may optionally determine and track retention rates of subjects in particular monitoring programs or types of monitoring programs. For example, if a particular subject has participated in three monitoring programs of a monitoring program (e.g., potentially different versions of the monitoring program including customized versions), the computer system 210 may determine and track a retention rate for the subject with respect to this particular monitoring program. The computer system 210 may similarly track the subject's compliance data and data quality data that correspond to this monitoring program.

The data quality data may include, for example, an indication of data quality for each of the subjects or for different categories of subjects. Additionally or alternatively, the data quality data may include data quality rates that indicate, for each of the subjects or for each category of subjects, the percent of data that meets minimum data quality requirements. The computer system 210 may use various factors to determine data quality or which may be used to set minimum data quality requirements. These factors can include response times (e.g., where relatively quick response times and/or relatively long response times may correspond to low data quality), sensor data accuracy (e.g., based on the sensor and/or device used to collect the sensor data), sensor data consistency (e.g., based on the sensor and/or device used to collect the sensor data, and/or the other sensor data values collected using the same sensor and/or device or the same sensor type and/or device type), response content (e.g., text input that is relatively short or that is below a threshold word count may correspond to low data quality; text input that is relatively long or that is above a threshold word count may correspond to high data quality; etc.), etc. The computer system 210 may use one or more algorithms to determine a data quality score or a data quality rate, e.g., for a particular monitoring program and/or across all monitoring programs. As an example, the computer system 210 may calculate a data quality score for each monitoring program of each subject or each category of subjects, and average the data quality scores to obtain an overall data quality score for the subject or the group of subjects.

Extracting at least one of the subject attribute data or the subject outcome data can include extracting at least one of the subject attribute data or the subject outcome data from a database. The database may store data sets for multiple different subjects, such as data sets for different devices and/or data sets for different users. The data sets can include attribute data for the different subjects. For example, where the subject is a device, a corresponding data set may include an ID assigned to the device, make of the device, a model of the device, a software version running on the device, a CPU speed of the device, a memory size of the device, an indication of sensors installed on the device (e.g., fingerprint scanner, GPS unit, Lidar, accelerometer, etc.), etc. The computer system 210 may extract all or a portion of this information from the data sets. Similarly, where the subject is a user, a corresponding data set may include the name of the user, an ID assigned to the user, demographic information of the user (e.g., age, race, ethnicity, gender, marital status, income, education, employment, residential state, etc.), non-demographic information of the user (e.g., past surgeries, medical conditions, genetics, lifestyle patterns, environmental factors, access to health care, etc.), an indication of what devices and/or sensors that user has access to, etc. The computer system 210 may extract all or a portion of this information from the data sets.

The data sets may include results of monitoring performed for the subjects using one or more remote computing devices. For example, the data sets may include an indication of messages sent to the subjects, responses received from the subjects, sensor data received from the subjects, etc. The data sets may additionally include information determined from received data and/or responses, such as subject outcomes. For example, the data sets may include response times, response frequency, message compliance or compliance rates for the user, an indication of user retention or retention rates for the user, indications of data quality, etc.

Extracting at least one of (i) the subject attribute data or (ii) the subject outcome data can include using metadata to identify the data in one or more data sets that should be extracted. For example, the computer system 210 may use an ID or data type to extract attribute data from the database 212. Similarly, the computer system 210 may use a different ID or data type to extract outcome data from the databased 212.

Extracting at least one of (i) the subject attribute data or (ii) the subject outcome data can include parsing through stored, monitored subject data to identify at least one of the subject attribute data or the subject outcome data. For example, the computer system 210 may store the monitored data in the database 212. In response to receiving instructions to start a new monitoring program, receiving instructions to update the profiles or groups, determining that monitoring data has been collected on one or more new subjects, and/or detecting a different event, the computer system 210 may parse through the data sets in the database 212 to identify the subject attribute data and/or the subject outcome data.

The process 1200 includes grouping the subjects into different groups according to levels of similarity (1204). The levels of similarity can be levels of similarity among the attributes of the subjects and/or the monitored outcomes for the subjects. As an example, with respect to FIG. 9A, the computer system 210 may determine or receive an indication of the key attributes 902 and the outcomes 904. The key attributes 902 may include a subset of particular subject attributes extracted by the computer system 210 from the subject data 950. Similarly, the outcomes 904 may include a subset of particular monitored outcomes for the subjects extracted by the computer system 210 from the monitoring outcome data 930. The computer system 210 may proceed to use at least one of the key attributes 902 and the outcomes 904 to group the subjects into different groups.

In grouping the subjects, the computer system 210 may group subjects based on the extracted attributes, the extracted outcomes, or a combination of the attributes and outcomes. As an example, the computer system 210 may use the extracted attributes to identify those subjects that share the same or a similar subset of subject attributes. In grouping the subjects, the computer system may additionally or alternatively identify those subjects that have demonstrated the same or similar outcomes. For example, the computer system 210 may identify those subjects that tend to have similar overall compliance rates (e.g., compliance rate are within a range of multiple ranges of compliance rates), similar device compliance rates, similar retention rates, produce similar data quality, etc. The computer system 210 may then further organize the subjects by those that have similar medical conditions or that share other attributes to identify multiple groups of subjects. For example, the computer system 210 may identify a first group of subjects that have diabetes and a compliance rate above 60%, a second group of subjects that have diabetes and a compliance rate below 60%, a third group of subjects that do not have diabetes and a compliance rate above 60%, and a fourth group of subjects that do not have diabetes and have a compliance rate below 60%.

In grouping the subjects, the computer system 210 may use one or more static or machine learning algorithms. For example, in some implementations, grouping the subjects into different groups according to levels of similarity includes grouping the subjects into different groups using one or more machine learning models. The one or more machine learning models can include a clustering machine learning model. For example, the computer system 210 can provide the key attributes 902 and/or the outcomes 904 to the clustering model 908 as input. The clustering model 908 may proceed to cluster the subjects (e.g., previous monitoring program participants) based on the key attributes 902 and/or the outcomes 904 into multiple groups of subjects. The clustering model 908 can cluster the subjects according to a subset of subject attributes and/or particular outcomes (e.g., desirable outcomes, undesirable outcomes, or certain types of outcomes, such as compliance rates, retention rates, or data quality).

Where a clustering machine learning model is used to group the subjects, the clustering model may be one of following models: a density clustering model, a connectivity clustering model, a centroid clustering model, distribution clustering model, a subspace clustering model, a group clustering model, a graph clustering model, signed-based clustering model, or a neural network model. In some cases, multiple machine learning models are used. As an example, two or more clustering models may be used to group the subjects.

In some implementations, the machine learning model(s) used for grouping subjects are trained (e.g., supervised) using input data sets and expected outputs for those data sets. The data sets may include, for example, subject attribute data and/or subject outcome data. The expected outputs for those data sets may include an indicator for each of the subjects that specifies which group that the subject belongs to. For example, the expected outputs may include values corresponding to subjects that fall within a first range of values corresponding to a first group of subjects, values corresponding to other subjects that fall within a second range of values corresponding to a second group of subjects, etc.

In some implementations, the machine learning model(s) used for grouping subjects are not-trained (e.g., unsupervised). For example, the machine learning model(s) may include an unsupervised k-means clustering algorithm that does not require ground truth in order to group the data points of the input data into distinct subgroups.

The output of the machine learning model may indicate a group that each of the subjects belong to. For example, the output of the machine learning model may include a value for a first subject that falls within a first range of values corresponding to a first group of subjects, and a second value for a second subject that falls within a second range of values corresponding to a second group of subjects. The output of the machine learning model may indicate one or more value ranges or thresholds that define the different groups. As another example, the output of the machine learning model may include a value for each subject, where the value corresponds to a particular group that the subject is placed in. In more detail, the output value may indicate the centroid that each subject was assigned to during grouping (e.g., clustering).

In some implementations, additional input is provided to the machine learning model. For example, with respect to FIG. 9A, the computer system 210 may provide cluster parameters 906 to the clustering model 908. The cluster parameters 906 may define a number of clusters, a minimum or maximum cluster size, a number of clustering iterations, a centroid change threshold, etc.

In some implementations, in grouping the subjects, the machine learning model performs multiple grouping iterations. For example, the machine learning model may be a k-means clustering algorithm that performs multiple clustering iterations until there is no change to the centroids or until the change to the centroids is below a threshold value.

The process 1200 includes constructing a profile for each of the groups (1206). A profile may represent a category of subjects. A profile may be constructed using one of the different groups of subjects. The resulting profile may represent a category of subjects that corresponds to one of the different groups of subjects. For example, after grouping the subjects into the different groups, the computer system 210 may construct corresponding profiles for the different groups. Each profile may be constructed using subject data from one of the different groups of subjects.

In some implementations, a profiles is constructed using two or more groups of subjects of the different groups. For example, the computer system 210 may combine multiple of the different groups of subjects, and used the combined groups to construct the profile such that the profile corresponds to multiple groups of the different groups of subjects.

Similarly, in some implementations, a profile is constructed using only a portion of one of the different groups. For example, the computer system 210 may split a group of subjects into two or more subgroups (e.g., based on input from a researcher or an administrator). The computer system 210 may proceed to use one of these subgroups to construct the profile such that the profile corresponds to the subgroup but does not correspond to all subjects in the larger group of subjects.

The computer system 210 may use subject data corresponding to one or more of the different groups to construct each of the profiles. The computer system 210 may use subject attribute data (e.g., not necessarily limited to the extracted subject attribute data) and subject outcome data (e.g., not necessarily limited to the extracted subject outcome data) to construct the profiles for each of the groups. In more detail, in constructing the profiles, the computer system 210 may use the subject attribute data and/or the subject outcome data to define inclusion criteria for each category of subjects corresponding to one of the different groups. For example, for a particular group of subjects, the computer system 210 may determine based on the attribute data that each subject in the group is over the age of 37, lives in an urban environment, and has been diagnosed with high blood pressure. Based on this, the computer system 210 may, in constructing a profile to represent a category of subjects corresponding to this group, set inclusion criteria for the group to require that subjects be above the age of 35, reside in an urban environment, and be diagnosed with high blood pressure or are observed to have at least 7/10 indicators for high blood pressure (e.g., based on a preliminary test, entry survey, etc.).

The set criteria may include a broader range of values than observed values, e.g., to make the group more inclusive. Alternatively, the set criteria may include a range of values that is the same range as the observed values. Similarly, the set criteria may include a range of values that is less than the observed values, e.g., to make the group more exclusive and/or to account for outliers in the group. Finally, the set criteria may include a combination of different range of values that are greater than, less than, and/or the same as the observed values.

In some implementations, the computer system 210 filters the different groups to identify a subset of groups in the different groups that meet criteria for profile construction. For example, in order to be used for profile construction, each of the different groups may need to include a minimum number of subjects. The groups may also need to meet other profile criteria, such as minimum diversity requirements. The profile criteria may be set by a researcher or an administrator. After determining the subset of groups in the different groups that meet the criteria for profile construction, the computer system 210 may use each of the groups in the subset of groups to construct corresponding profiles.

In some implementations, the inclusion criteria is used to determine which subjects correspond to which profiles. For example, although the subjects were previously assigned to groups, the inclusion criteria may differ from the observed values of the group. Accordingly, the computer system 210 may compare the inclusion criteria against the attribute data and/or outcome data of the subjects to determine or verify which subjects correspond to which profiles. The attribute data and/or outcome data of the subjects used by the computer system 210 used to determine or verify which subjects correspond to which profiles may include attribute data and/or outcome data of active subjects (e.g., subjects that have participated in a monitoring program or monitoring program over the last year, have participated in a monitoring program or monitoring program over the last two years, are listed as active, etc.). In contrast, the attribute data and/or outcome data used to construct the profiles may include attribute data and/or outcome data of all subjects in a subject pool (e.g., for which monitored data has been collected), including active and inactive subjects.

In some implementations, information is added to the profiles that is not used as inclusion criteria. For example, the computer system 210 may include subject attribute data and/or attribute data statistics in the profile. In more detail, the computer system 210 may include the number or percent of subjects in the profile that have a particular attribute (e.g., percent of subjects that have high blood pressure, that are of a first ethnicity, that have a college education, etc.), and/or that demonstrate the same or similar outcomes (e.g., that have a retention rate greater than 75%, that have a retention rate between 50% and 75%, and that have a retention rate less than 50%). The computer system 210 may also determine and include in the profile an indication of the number of subjects in the category of subjects, or a percent of the total or active subject pool that the category of subjects represents. For example, the computer system 210 may compare the number of subjects in a category of subjects represented by a profile to the total number of subjects or to a total number of active subjects to determine that the category of subjects represents 4.2% of the subject pool or of the active subject pool.

The process 1200 includes using data sets to characterize, for each profile, differing effects of elements of monitoring programs on program compliance outcomes (1208). The data sets may be stored in a database by, for example, the computer system 210. The data sets may include monitored subject data over multiple monitoring programs. In determining the different effects of elements of monitoring programs, such as tasks (e.g., tests, appointments, exercises, etc.), message content, message frequency, message time, task or event schedule, etc., on subjects' outcomes, the computer system 210 may use the program data 940 to identify various monitoring program elements and use the monitoring outcome data 930 to identify the effects of those program elements on the different categories of subjects. In more detail, for a particular monitoring program element, the computer system 210 may determine which subjects have encountered that element before and in which past monitoring program(s), and use a portion of the outcome data 930 corresponding to those past monitoring programs and subjects to determine what effect on subject outcomes, if any, the particular element had on those subjects.

For example, if three subjects assigned to a first profile are determined to have encountered the requirement for weekly office visits in one or more prior monitoring programs, the computer system 210 may obtain a portion of the monitoring outcome data 930 corresponding to those monitoring programs for the first profile subjects. The computer system 210 may proceed to use the portion of the outcome data 930 and the corresponding subject data 950 to determine (e.g., to see if there was a deviation or a statistically significant deviation from the first profile subject's typical behavior) if the weekly office visits had a negative, positive, or neutral effect on compliance rates, retention rates, and/or data quality for the first profile subjects, and the magnitude of that effect on those subject outcomes. The computer system 210 may proceed to include the effect (e.g., direction and magnitude) as part of the profile

As discussed above, the effect of a monitoring program element on subject outcomes may be in the form of calculated impact scores as shown and described above with respect to FIGS. 10-11. An impact score may be an absolute or relative score. This score may represent a percentage or a percentage range. The percentage or percentage range that the impact score represents may depend on the magnitude of the impact score and/or on the corresponding outcome (e.g., larger percent range for compliance rates than, for example, retention rates which are likely to fluctuate to a lesser degree than compliance rates).

In some implementations, the computer system 210 analyzes the communication behavior of the subjects of a profile. This analysis may be a separate analysis from analyzing the effect of study parameters, or may be part of that analysis. In analyzing the communication behavior, the computer system 210 may determine the preferred communication channel, communication frequency, communication time, communication content, communication vocabulary (e.g., word choice), or communication sentence structure for the profile's subjects. The computer system 210 may further determine the effects of particular communication attributes (e.g., channel, frequency, time sent, etc.) on the outcomes of the profile's subjects, e.g., when compared to other communication attributes. For example, the computer system 210 may determine that the Profile 1 subjects prefer communication by SMS text message over email. The computer system 210 may analyze the subject data 250 to determine that, when compared to email, the Profile 1 subjects are 35% more likely to respond to SMS text message.

The computer system 210 may update profiles over time using monitored data. For example, the computer system 210 may reanalyze the effects of study parameters on profile subjects using updated subject data obtained in one or more ongoing monitoring programs or results from recently completed monitoring programs. The computer system 210 may perform this analysis after a triggering event is detected, such as the passing of a predetermined amount of time, after a threshold amount of monitored data is collected, after a monitoring program ends, etc. Similarly, the computer system 210 may use the clustering model 908 to cluster subjects after a triggering event is detected (e.g., after a predetermined amount of time has passed, after a threshold amount of monitored data is collected, after a threshold number of new subjects have appeared/joined, etc.). The computer system 210 may proceed to analyze the clusters in the manner described above. Additionally or alternatively, the computer system 210 may avoid additional analysis of a cluster (e.g., to improve efficiency, reduce processor load, increase processing speed, etc.) if it is determined that the members for a particular cluster match the membership for a previously determined cluster.

In some implementations, the computer system 210 analyzes the effects of each element of every monitoring program that has been run in a sessions. Because various monitoring programs and monitoring programs may include all or a portion of the same elements, the computer system 210 does not necessarily need to analyze each and every element of each monitoring program (or version of monitoring program) separately.

In some implementations, the computer system 210 analyzes the effects of only a subset of elements of the monitoring programs that have been run in one or more monitoring programs. For example, the computer system 210 may choose to analyze the effects of only those elements for which sufficient monitored data has been collected on. In more detail, the computer system 210 may choose to only analyze the effects of those elements that have been in at least three monitoring programs, for which there have been at least six months of data collected for, for which at least twenty unique subjects experienced, etc.

In analyzing the effects of the monitoring program elements on subject outcomes, the computer system 210 may extract, for each profile, the relevant information from the database 212, and analyze the extracted data together. That is, instead of analyzing the effects on a subject by subject basis, the computer system 210 may collect, for each of the profiles, all of the relevant subject data and analyze the collected data as a whole. This has the benefit if reducing computational burden by reducing the number of processes the CPU(s) of the computer system 210 need to perform, and further increases efficiency by speeding up the processing time.

After analyzing the effects of the monitoring program elements on subject outcomes, the computer system 210 may update the profiles to include the corresponding analysis results. For example, if it is determined that subjects in a category of subjects represented by Profile 1 are twice as likely to not complete a study if it requires blood to be drawn daily, the computer system 210 may update Profile 1 to include an impact score of −5 (e.g., to represent −50% effect on retention rate) for retention for the monitoring program element, “blood drawn” with a frequency of “daily.”

The computer system 210 may organize the different effects of different elements into different categories or hierarchies within the profiles. For example, with respect to FIG. 11, the computer system 210 may organize the data in the table 1104 b and the table 1104 c under a communication category.

The process 1200 includes using the characterization data to create or adjust a monitoring program involving communicating with a selected set of remote devices (1210). The computer system 210 may identify the profiles that are present among a monitoring group selected for a monitoring program, compare the elements of the selected monitoring program to the element effect information in the profiles, and, based on the comparison, determine one or more adjustments to the monitoring program for the different categories of subjects. The adjustments made by the computer system 210 may include removing or modifying an element of a monitoring program (e.g., reduce number of tests that user must complete if this is determined to significantly lower retention rates), adjusting communication attributes (e.g., communication channel, frequency, time, content, sentence structure, etc.), or adding an element (e.g., account for subjects not having access to a device, account for subject not having access to transportation by providing transportation credit, etc.). For example, if an element of a monitoring program is anticipated to reduce compliance with subjects assigned to a second profile, the computer system 210 may adjust that element using information in the second profile to mitigate the anticipated reduced compliance.

The computer system 210 can automatically make the adjustments for the one or more groups of subjects enrolled in the monitoring program. For example, if the computer system 210 determines that a particular adjustment should account for an anticipated lower retention rate among Profile 1 subject due to an element of the monitoring program, the computer system 210 may generate a new version of the monitoring program and distribute this new version to only remote devices corresponding to the Profile 1 subjects.

The computer system 210 can generate a recommendation to make one or more adjustments and transmit the recommendation to a researcher or an administrator. The recommendation may include multiple recommended adjustments to the monitoring program, such as one or more recommended adjustments for each unique profile present among the selected subjects for the monitoring program. The recommendation may include the most recommended adjustments, such as the five or ten most recommended adjustments. Similarly, the computer system 210 may generate a recommendation that includes at least two recommendations (e.g., two most recommended adjustments) for each of the profiles present among the selected subjects. The computer system 210 may rank the recommended adjustments, e.g., based on predicted effect at achieving a successful monitoring program (e.g., obtaining viable data). For example, the adjustments predicted by the computer system 210 to have the largest remedial effect (e.g., for expected negative outcomes, such as low retention, low compliance, low data quality, etc.) may be ranked above adjustments that are predicted to have less significant effects. Similarly, adjustments that are predicted by the computer system 210 to have a higher likelihood of producing a remedial effect may be ranked by the computer system 210 above adjustments whose beneficial effect is more speculative.

The computer system 210 may wait for a response from the researcher or administrator before proceeding with the monitoring program. The response may include a confirmation (e.g., if it included one recommended adjustment, or one recommended adjustment for each category of subjects represented in the monitoring group). Alternatively, the response may include one or more selections, e.g., that correspond to adjustments selected by the researcher or administrator. The computer system 210 may use the confirmation or response to generate one or more additional versions of the monitoring program, each specific to a particular category of subjects (e.g., corresponding to a particular profile).

In some implementations, if the computer system 210 does not receive a response within a predetermined amount of time, the computer system may implement one or more recommended adjustments itself. For example, for each profile, the computer system 210 may generate a version of the monitoring program where the most recommended adjustment has been made (e.g., if there are recommended adjustments). If there were no recommended adjustments for a particular category of subjects, the computer system 210 may send the default monitoring program to the remote devices of those subjects.

Using the characterization data to create or adjust the monitoring program may also include using the characterization data to adjust the enrolled subjects. For example, if the characterization data indicates that a subset of the enrolled subjects are predicted to have insufficient compliance rates through a session due to the monitoring program requirements (e.g., if the program requirements cannot be adjusted or cannot be adjusted to the extent needed), the computer system 210 may replace the subset of the enrolled subjects with a different set of subjects who are anticipated to produce outcomes that are sufficient with the monitoring program's requirements. The computing system 210 may automatically enroll the other subjects, or may send invitations to the other subjects to enroll.

As another example, the computer system 210 may use the profiles to determine that additional subjects need to be added to the monitoring group due to one or more profiles not being represented. That is, the profiles may be used as a diversity metric. The computer system 210 may, for example, determine that one or more additional subjects should be enrolled or invited to the monitoring program based on the size of the category of subjects that is not represented. For example, the computer system 210 may automatically enroll or generate a recommendation for enrollment of a subject that is in a category of subjects (e.g., corresponding to a particular profile) that represents more than 3%, 5%, or 7% of the subject pool and is not represented in the monitoring group for the current monitoring program. Similarly, certain categories of subjects may be marked as necessary for monitoring program representation for other reasons, such as for meeting diversity criteria required for the monitoring program and/or for obtaining viable results.

Communication with a selected set of remote devices may take place over a communication network such as a wireless internet network (e.g., Wi-Fi), a cellular network (e.g., 5G network), etc. The computer system 210 may form a secure communication channel, such as an encrypted channel between the remote devices and the computer system 210 to protect the transfer of sensitive data such as medical records, medical information, health data, photographs, etc. between the remote devices and the computer system 210.

Prior to starting a monitoring program, the computer system 210 may distribute software to the remote devices. Alternatively, the remote devices may access the software through a website or mobile application. However, the accessed software may be customized to the specific remote device based on it or its user corresponding to a particular subject profile. The software may provide for the configuration of a secure communication channel between a remote device and the computer system 210, and/or for a user interface through which a subject can interact to, for example, respond to messages, provide feedback, provide text input, submit photographs, submit test results, etc. using touch input, keyboard input, voice input, or a combination of different input types.

As previously mentioned, the computer system 210 may customize the monitoring program for different categories of subjects corresponding to different profiles. In doing this, the computer system 210 may generate and distribute different software versions. The different software versions may provide for different and possibly unique interactions between devices of subjects corresponding to a first profile, and devices of subjects corresponding to a second profile. For example, the two different software versions corresponding to the same base monitoring program may provide for a different frequency of data collection, using a different sensor to collect the data, a different channel of communication to send request or message, etc.

In some implementations, the remote devices are the subjects for a monitoring program.

FIG. 13 is a flowchart diagram that illustrates an example process 1200 for assessing and selecting technologies. The process 1300 may be performed by one or more computers, such as the computer system 210 shown in various figures including FIG. 2. The operations of the process 1300 may be distributed among one or more servers, one or more client devices, and/or other computing systems. For example, the operations of the process 1300 may be performed by a management and distribution system, such as the system 210, that includes one or more servers, one or more client devices, and/or other computing systems.

The process 1300 includes accessing subject data in a database for subjects that have been involved in one or more monitoring program (1302). For example, with respect to FIG. 9A, the computer system 210 may access the subject data 950 from the database 212. The subject data 950 may include attributes for subjects in a subject pool that have participated in one or more previous monitoring programs, and/or are currently enrolled in an ongoing monitoring program. The subject data 950 may also include other data, such as historical data, including response times, response content, compliance data, uploaded data, indication of past and/or present monitoring programs that subject has participated in, etc.

The process 1300 includes determining subsets of the subjects based on a degree of similarity in at least attributes of the subjects or monitored outcomes for the subjects (1304). For example, with respect to FIG. 9A, the computer system 210 may use key attributes 902 (e.g., a subset of particular subject attributes selected by the computer system 210 or a researcher) and/or outcomes 904 (e.g., one or more particular outcomes selected by the computer system 210 or a researcher). The computer system 210 may group the subjects using the key attributes 902 and/or the outcomes 904. As an example, the computer system 210 may provide the key attributes 902, the outcomes 904, and subject data for a subject pool to the clustering model 908 as input. The clustering model 908 may proceed to organize the subjects in the subject pool based on the key attributes 902 if that is provided as input, based on the particular outcomes (e.g., data quality, overall compliance rates, compliance rates in a particular category, retention rates, etc.) in the outcomes 904 if that is provided as input the clustering model 908, or based on the key attributes 902 and the outcomes 904 if both are provided as input to the clustering model 908.

The output of the clustering model 908 may include an indication of which cluster each of the subjects in the subject pool belongs to. For example, the output of the clustering model 908 may be an indication of which centroid each subject was assigned to.

The process 1300 includes defining a plurality of profiles based on the determined subsets (1306). For example, the computer system 210 may use each of the groups of the subjects (e.g., the clusters) to a profile that represents a category of subjects. In generating the profiles, the computer system 210 may retrieve information from the subjects in the groups and use the information to define inclusion criteria for the different profiles. The inclusion criteria may be defined by the computer system 210 such that it is mutually exclusive with respect to the inclusion criteria of the other profiles, such that the a subject cannot be assigned to multiple profiles. However, in some cases, a subject may be assigned to multiple profiles.

The process 1300 includes generating impact characterization data indicating different levels of impact that elements of monitoring programs have on outcomes for subjects (1308). The computer system 210 may identify the profiles that are present among a monitoring group selected for a monitoring program, compare the elements of the selected monitoring program to the element effect information in the profiles, and, based on the comparison, determine one or more adjustments to the monitoring program for the different categories of subjects. The adjustments made by the computer system 210 may include removing or modifying an element of a monitoring program (e.g., reduce number of tests that user must complete if this is determined to significantly lower retention rates), adjusting communication attributes (e.g., communication channel, frequency, time, content, sentence structure, etc.), or adding an element (e.g., account for subjects not having access to a device, account for subject not having access to transportation by providing transportation credit, etc.). For example, if an element of a monitoring program is anticipated to reduce compliance with subjects assigned to a second profile, the computer system 210 may adjust that element using information in the second profile to mitigate the anticipated reduced compliance.

The process 1300 includes using the plurality of profiles and the generated impact characterization data to create a monitoring program or adjust a monitoring program (1310). For example, the computer system 210 may customize a monitoring program selected by a researcher to improve the likelihood of viable data being produced as a result of the monitoring program, and/or other goals of the monitoring program being obtained (e.g., such as diversity goals to increase the applicability of the results of the monitoring program). The computer system 210 may create and/or customize a monitoring program for each profile among the enrolled monitoring program subjects. Accordingly, the computer system 210 may first identify what profiles are present among the monitoring group, prior to (i) adjusting the monitoring group and/or (ii) the elements of the monitoring program.

Embodiments of the invention and all of the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the invention can be implemented as one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a tablet computer, a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the invention can be implemented on a computer having a display device, e.g., a cathode ray tube or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

Embodiments of the invention can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of the invention. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

In each instance where an HTML file is mentioned, other file types or formats may be substituted. For instance, an HTML file may be replaced by an XML, JSON, plain text, or other types of files. Moreover, where a table or hash table is mentioned, other data structures (such as spreadsheets, relational databases, or structured files) may be used.

Particular embodiments of the invention have been described. Other embodiments are within the scope of the following claims. For example, the steps recited in the claims can be performed in a different order and still achieve desirable results. 

What is claimed is:
 1. A machine learning system for improving monitoring outcomes, the system including: a subject database storing a data set for each of multiple subjects, wherein, for each of the multiple subjects, the data set for the subject comprises results of monitoring performed for the subject using one or more remote devices; a network interface to receive monitoring data from the remote devices; and a server system configured to manage monitoring of the remote devices, the server system being configured to perform operations comprising: extracting, from the subject database, at least one of (i) subject attribute data describing characteristics of the subjects or (ii) subject outcome data including results from monitoring programs that involved the subjects; grouping the subjects, based on the extracted data from the database, into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects; constructing a profile for each of the groups, wherein constructing the profile for each group comprises defining inclusion criteria for the group including attribute values or ranges of attribute values needed for membership in the group that are determined based on aggregate data for the subjects in the group; using the data sets in the subject database to characterize, for each profile, differing effects that different elements of monitoring programs have on compliance, by subjects that satisfy the inclusion criteria of the profile, in performing activities that participants in the monitoring programs are requested to perform during the monitoring programs; and for a particular monitoring program involving communication between the server system and remote devices over a communication network, selectively distributing software or configuration information to the remote devices over the communication network based on the characterization data, wherein the server system provides the software or configuration information to a subset of the remote devices in a manner that changes data collection parameters of the remote devices in the subset of the remote devices, thereby altering data collection performed for the particular monitoring program by the remote devices in the subset.
 2. The system of claim 1, wherein grouping the subjects into the different groups comprises: providing at least one of the attributes of the subjects or the monitored outcomes for the subjects as input to a machine learning model; and using the machine learning model to cluster the subjects into different clusters based on at least one of the attributes of the subjects or the monitored outcomes for the subjects, wherein the different groups of subjects are derived from the clusters.
 3. The system of claim 2, wherein the machine learning model comprises a clustering model.
 4. The system of claim 3, wherein the clustering model is a k-means clustering model.
 5. The system of claim 3, wherein the clustering model is a density clustering model, a connectivity clustering model, a centroid clustering model, distribution clustering model, a subspace clustering model, a group clustering model, a graph clustering model, signed-based clustering model, or a neural network model.
 6. The system of claim 1, wherein the operations comprise: identifying a set of elements of the particular monitoring program, the elements indicating characteristics or requirements of the particular monitoring program; determining that a subset of individuals participating in the monitoring program satisfy a particular profile of the constructed profiles; determining, based on the characterization data, that an element of the particular monitoring program is predicted to reduce compliance of the individuals in the subset with one or more activities that participants in the particular monitoring program are requested to perform; and based on determining that the element of the particular monitoring program is predicted to reduce compliance of individuals in the subset, changing communication of the server system with remote devices of the individuals in the subset to at least partially offset the predicted reduction in compliance.
 7. A method performed by one or more computers, the method comprising: extracting, from a subject database, at least one of (i) subject attribute data describing characteristics of subjects or (ii) subject outcome data including results from monitoring programs that involved the subjects; grouping the subjects, based on the extracted data from the database, into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects; constructing a profile for each of the groups, wherein constructing the profile for each group comprises defining inclusion criteria for the group including attribute values or ranges of attribute values needed for membership in the group that are determined based on aggregate data for the subjects in the group; using the data sets in the subject database to characterize, for each profile, differing effects that different elements of monitoring programs have on compliance, by subjects that satisfy the inclusion criteria of the profile, in performing activities that participants in the monitoring programs are requested to perform during the monitoring programs; and for a particular monitoring program involving communication between the one or more computers and remote devices over a communication network, selectively distributing software or configuration information to the remote devices over the communication network based on the characterization data, wherein the one or more computers provide the software or configuration information to a subset of the remote devices in a manner that changes data collection parameters of the remote devices in the subset of the remote devices, thereby altering data collection performed for the particular monitoring program by the remote devices in the subset.
 8. The method of claim 7, comprising adding a study element to the particular monitoring program based on the characterization data, wherein the study element is added to compensate for a predicted impact of at least one other element in the particular monitoring program.
 9. The method of claim 7, comprising customizing the particular monitoring program for each of multiple of the different profiles based on portions of the characterization data corresponding to each of the different profiles.
 10. The method of claim 7, comprising storing portions of the characterization data corresponding to the different profiles in each of the profiles.
 11. One or more non-transitory computer-readable media storing instructions that are operable, when executed by the one or more computers, cause the one or more computers to perform operations comprising: extracting, from a subject database, at least one of (i) subject attribute data describing characteristics of subjects or (ii) subject outcome data including results from monitoring programs that involved the subjects; grouping the subjects, based on the extracted data from the database, into different groups according to levels of similarity among attributes of the subjects or monitored outcomes for the subjects; constructing a profile for each of the groups, wherein constructing the profile for each group comprises defining inclusion criteria for the group including attribute values or ranges of attribute values needed for membership in the group that are determined based on aggregate data for the subjects in the group; using the data sets in the subject database to characterize, for each profile, differing effects that different elements of monitoring programs have on compliance, by subjects that satisfy the inclusion criteria of the profile, in performing activities that participants in the monitoring programs are requested to perform during the monitoring programs; and for a particular monitoring program involving communication between the one or more computers and remote devices over a communication network, selectively distributing software or configuration information to the remote devices over the communication network based on the characterization data, wherein the one or more computers provide the software or configuration information to a subset of the remote devices in a manner that changes data collection parameters of the remote devices in the subset of the remote devices, thereby altering data collection performed for the particular monitoring program by the remote devices in the subset.
 12. The method of claim 7, wherein the elements of monitoring programs include at least one of monitoring program duration, data collection frequency, types of data collected, types of sensors used, or types of user interactions or user inputs needed, wherein the characterization data indicates differences in the impact of the respective elements on subjects satisfy the inclusion criteria for different profiles.
 13. The method of claim 7, wherein grouping the subjects comprises clustering the subjects according to similarity of the subjects to each other across multiple dimensions including (i) one or more attributes of the subjects and (ii) one or more monitored outcomes for the subjects in one or more monitoring programs.
 14. The method of claim 7, wherein grouping the subjects comprises grouping the subjects according to levels of compliance that the subjects achieved in performing activities required for participation in one or more monitoring programs.
 15. The method of claim 7 wherein grouping the subjects comprises grouping the subjects according to levels of data quality that the subjects achieved in performing activities required for participation in one or more monitoring programs.
 16. The method of claim 7, wherein selectively distributing the software or configuration information to the remote devices over the communication network comprises, providing, to a subset of the remote devices that are each determined to be associated with users that satisfy the inclusion criteria for a particular one of the constructed profiles, software or configuration data that changes a data collection parameter specifying at least one of: a communication channel used for data collection; a sensor used for data collection; a source of data to provide over the communication network; or a frequency or schedule for performing data collection. 